1855 lines
1.8 MiB
1855 lines
1.8 MiB
<!DOCTYPE html>
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<!-- Created by pdf2htmlEX (https://github.com/pdf2htmlEX/pdf2htmlEX) -->
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<html xmlns="http://www.w3.org/1999/xhtml">
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<head>
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<meta charset="utf-8"/>
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<meta name="generator" content="pdf2htmlEX"/>
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<meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1"/>
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<style type="text/css">
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/*!
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* Base CSS for pdf2htmlEX
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* Copyright 2012,2013 Lu Wang <coolwanglu@gmail.com>
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* https://github.com/pdf2htmlEX/pdf2htmlEX/blob/master/share/LICENSE
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*/#sidebar{position:absolute;top:0;left:0;bottom:0;width:250px;padding:0;margin:0;overflow:auto}#page-container{position:absolute;top:0;left:0;margin:0;padding:0;border:0}@media screen{#sidebar.opened+#page-container{left:250px}#page-container{bottom:0;right:0;overflow:auto}.loading-indicator{display:none}.loading-indicator.active{display:block;position:absolute;width:64px;height:64px;top:50%;left:50%;margin-top:-32px;margin-left:-32px}.loading-indicator img{position:absolute;top:0;left:0;bottom:0;right:0}}@media print{@page{margin:0}html{margin:0}body{margin:0;-webkit-print-color-adjust:exact}#sidebar{display:none}#page-container{width:auto;height:auto;overflow:visible;background-color:transparent}.d{display:none}}.pf{position:relative;background-color:white;overflow:hidden;margin:0;border:0}.pc{position:absolute;border:0;padding:0;margin:0;top:0;left:0;width:100%;height:100%;overflow:hidden;display:block;transform-origin:0 0;-ms-transform-origin:0 0;-webkit-transform-origin:0 0}.pc.opened{display:block}.bf{position:absolute;border:0;margin:0;top:0;bottom:0;width:100%;height:100%;-ms-user-select:none;-moz-user-select:none;-webkit-user-select:none;user-select:none}.bi{position:absolute;border:0;margin:0;-ms-user-select:none;-moz-user-select:none;-webkit-user-select:none;user-select:none}@media print{.pf{margin:0;box-shadow:none;page-break-after:always;page-break-inside:avoid}@-moz-document url-prefix(){.pf{overflow:visible;border:1px solid #fff}.pc{overflow:visible}}}.c{position:absolute;border:0;padding:0;margin:0;overflow:hidden;display:block}.t{position:absolute;white-space:pre;font-size:1px;transform-origin:0 100%;-ms-transform-origin:0 100%;-webkit-transform-origin:0 100%;unicode-bidi:bidi-override;-moz-font-feature-settings:"liga" 0}.t:after{content:''}.t:before{content:'';display:inline-block}.t span{position:relative;unicode-bidi:bidi-override}._{display:inline-block;color:transparent;z-index:-1}::selection{background:rgba(127,255,255,0.4)}::-moz-selection{background:rgba(127,255,255,0.4)}.pi{display:none}.d{position:absolute;transform-origin:0 100%;-ms-transform-origin:0 100%;-webkit-transform-origin:0 100%}.it{border:0;background-color:rgba(255,255,255,0.0)}.ir:hover{cursor:pointer}</style>
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<style type="text/css">
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/*!
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* Fancy styles for pdf2htmlEX
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* Copyright 2012,2013 Lu Wang <coolwanglu@gmail.com>
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* https://github.com/pdf2htmlEX/pdf2htmlEX/blob/master/share/LICENSE
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*/@keyframes fadein{from{opacity:0}to{opacity:1}}@-webkit-keyframes fadein{from{opacity:0}to{opacity:1}}@keyframes swing{0{transform:rotate(0)}10%{transform:rotate(0)}90%{transform:rotate(720deg)}100%{transform:rotate(720deg)}}@-webkit-keyframes swing{0{-webkit-transform:rotate(0)}10%{-webkit-transform:rotate(0)}90%{-webkit-transform:rotate(720deg)}100%{-webkit-transform:rotate(720deg)}}@media screen{#sidebar{background-color:#2f3236;background-image:url("data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSI0IiBoZWlnaHQ9IjQiPgo8cmVjdCB3aWR0aD0iNCIgaGVpZ2h0PSI0IiBmaWxsPSIjNDAzYzNmIj48L3JlY3Q+CjxwYXRoIGQ9Ik0wIDBMNCA0Wk00IDBMMCA0WiIgc3Ryb2tlLXdpZHRoPSIxIiBzdHJva2U9IiMxZTI5MmQiPjwvcGF0aD4KPC9zdmc+")}#outline{font-family:Georgia,Times,"Times New Roman",serif;font-size:13px;margin:2em 1em}#outline ul{padding:0}#outline li{list-style-type:none;margin:1em 0}#outline li>ul{margin-left:1em}#outline a,#outline a:visited,#outline a:hover,#outline a:active{line-height:1.2;color:#e8e8e8;text-overflow:ellipsis;white-space:nowrap;text-decoration:none;display:block;overflow:hidden;outline:0}#outline a:hover{color:#0cf}#page-container{background-color:#9e9e9e;background-image:url("data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSI1IiBoZWlnaHQ9IjUiPgo8cmVjdCB3aWR0aD0iNSIgaGVpZ2h0PSI1IiBmaWxsPSIjOWU5ZTllIj48L3JlY3Q+CjxwYXRoIGQ9Ik0wIDVMNSAwWk02IDRMNCA2Wk0tMSAxTDEgLTFaIiBzdHJva2U9IiM4ODgiIHN0cm9rZS13aWR0aD0iMSI+PC9wYXRoPgo8L3N2Zz4=");-webkit-transition:left 500ms;transition:left 500ms}.pf{margin:13px auto;box-shadow:1px 1px 3px 1px #333;border-collapse:separate}.pc.opened{-webkit-animation:fadein 100ms;animation:fadein 100ms}.loading-indicator.active{-webkit-animation:swing 1.5s ease-in-out .01s infinite alternate none;animation:swing 1.5s ease-in-out .01s infinite alternate none}.checked{background:no-repeat url(data:image/png;base64,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)}}</style>
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<style type="text/css">
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.ff0{font-family:sans-serif;visibility:hidden;}
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(function(){/*
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pdf2htmlEX.js: Core UI functions for pdf2htmlEX
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*/
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var pdf2htmlEX=window.pdf2htmlEX=window.pdf2htmlEX||{},CSS_CLASS_NAMES={page_frame:"pf",page_content_box:"pc",page_data:"pi",background_image:"bi",link:"l",input_radio:"ir",__dummy__:"no comma"},DEFAULT_CONFIG={container_id:"page-container",sidebar_id:"sidebar",outline_id:"outline",loading_indicator_cls:"loading-indicator",preload_pages:3,render_timeout:100,scale_step:0.9,key_handler:!0,hashchange_handler:!0,view_history_handler:!0,__dummy__:"no comma"},EPS=1E-6;
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<title></title>
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<body>
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||
<div id="sidebar">
|
||
<div id="outline">
|
||
<ul><li><a class="l" href="#pf7" data-dest-detail='[7,"XYZ",28.346,255.118,null]'>Compiler Optimizations</a><ul><li><a class="l" href="#pfb" data-dest-detail='[11,"XYZ",28.346,228.21,null]'>About the Compiler</a></li><li><a class="l" href="#pfd" data-dest-detail='[13,"XYZ",28.346,228.21,null]'>Compiler Optimization Flags</a></li><li><a class="l" href="#pff" data-dest-detail='[15,"XYZ",28.346,228.21,null]'>Floating-point Optimization Flags</a></li><li><a class="l" href="#pf11" data-dest-detail='[17,"XYZ",28.346,228.21,null]'>Linker Optimization Flags</a></li><li><a class="l" href="#pf12" data-dest-detail='[18,"XYZ",28.346,215.913,null]'>Architecture Flags</a></li><li><a class="l" href="#pf15" data-dest-detail='[21,"XYZ",28.346,216.354,null]'>Help the Compiler to Produce Better Code</a></li><li><a class="l" href="#pf16" data-dest-detail='[22,"XYZ",28.346,224.12,null]'>Profile Guided Optimization (PGO)</a></li><li><a class="l" href="#pf19" data-dest-detail='[25,"XYZ",28.346,228.21,null]'>Post-Processing Binary Optimizer</a></li></ul></li><li><a class="l" href="#pf1c" data-dest-detail='[28,"XYZ",28.346,255.118,null]'>Compiler Transformation Techniques</a><ul><li><a class="l" href="#pf1e" data-dest-detail='[30,"XYZ",28.346,228.21,null]'>Basic Compiler Transformations</a></li><li><a class="l" href="#pf21" data-dest-detail='[33,"XYZ",28.346,228.21,null]'>Loop Unswitching</a></li><li><a class="l" href="#pf22" data-dest-detail='[34,"XYZ",28.346,220.666,null]'>Loop Fusion</a></li><li><a class="l" href="#pf23" data-dest-detail='[35,"XYZ",28.346,212.502,null]'>Loop Fission</a></li><li><a class="l" href="#pf24" data-dest-detail='[36,"XYZ",28.346,220.043,null]'>Loop Interchange</a></li><li><a class="l" href="#pf25" data-dest-detail='[37,"XYZ",28.346,220.342,null]'>Loop Tiling</a></li></ul></li><li><a class="l" href="#pf26" data-dest-detail='[38,"XYZ",28.346,255.118,null]'>Libraries and Data Structures</a><ul><li><a class="l" href="#pf27" data-dest-detail='[39,"XYZ",28.346,228.21,null]'>External Libraries</a></li></ul></li><li><a class="l" href="#pf2b" data-dest-detail='[43,"XYZ",28.346,255.118,null]'>Performance Benchmarking</a><ul><li><a class="l" href="#pf2d" data-dest-detail='[45,"XYZ",28.346,216.663,null]'>What to Test?</a></li><li><a class="l" href="#pf2e" data-dest-detail='[46,"XYZ",28.346,228.21,null]'>Workload/Dataset Quality</a></li><li><a class="l" href="#pf2f" data-dest-detail='[47,"XYZ",28.346,207.494,null]'>Cache Behavior</a></li><li><a class="l" href="#pf31" data-dest-detail='[49,"XYZ",28.346,228.21,null]'>Stable CPU Performance</a></li><li><a class="l" href="#pf35" data-dest-detail='[53,"XYZ",28.346,206.653,null]'>Multi-Threads Considerations</a></li><li><a class="l" href="#pf36" data-dest-detail='[54,"XYZ",28.346,228.21,null]'>Program Memory Layout</a></li><li><a class="l" href="#pf37" data-dest-detail='[55,"XYZ",28.346,219.917,null]'>Measurement Overhead</a></li><li><a class="l" href="#pf38" data-dest-detail='[56,"XYZ",28.346,228.21,null]'>Compiler Optimizations</a></li><li><a class="l" href="#pf3a" data-dest-detail='[58,"XYZ",28.346,164.531,null]'>Metric Evaluation</a></li></ul></li><li><a class="l" href="#pf40" data-dest-detail='[64,"XYZ",28.346,255.118,null]'>Profiling</a><ul><li><a class="l" href="#pf42" data-dest-detail='[66,"XYZ",28.346,228.21,null]'>gprof</a></li><li><a class="l" href="#pf44" data-dest-detail='[68,"XYZ",28.346,228.21,null]'>uftrace</a></li><li><a class="l" href="#pf45" data-dest-detail='[69,"XYZ",28.346,222.194,null]'>callgrind</a></li><li><a class="l" href="#pf46" data-dest-detail='[70,"XYZ",28.346,228.21,null]'>cachegrind</a></li><li><a class="l" href="#pf49" data-dest-detail='[73,"XYZ",28.346,228.21,null]'>perf Linux profiler</a></li></ul></li><li><a class="l" href="#pf4c" data-dest-detail='[76,"XYZ",28.346,255.118,null]'>Parallel Computing</a><ul><li><a class="l" href="#pf4d" data-dest-detail='[77,"XYZ",28.346,217.069,null]'>Concurrency vs. Parallelism</a></li><li><a class="l" href="#pf4e" data-dest-detail='[78,"XYZ",28.346,211.032,null]'>Performance Scaling</a></li><li><a class="l" href="#pf4f" data-dest-detail='[79,"XYZ",28.346,228.21,null]'>Gustafson's Law</a></li><li><a class="l" href="#pf50" data-dest-detail='[80,"XYZ",28.346,228.21,null]'>Parallel Programming Languages</a></li></ul></li></ul></div>
|
||
</div>
|
||
<div id="page-container">
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<div id="pf2" class="pf w0 h0" data-page-no="2"><div class="pc pc2 w0 h0"><img class="bi x0 y0 w1 h1" alt="" 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class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">T<span class="_ _8"></span>able<span class="_ _9"> </span>of<span class="_ _a"> </span>Contents</div><div class="t m0 x5 h7 y8 ff1 fs4 fc2 sc0 ls0 ws0">1<span class="_ _b"> </span><span class="fs2 fc0">Compiler<span class="_ _7"> </span>Optimizations</span></div><div class="t m0 x6 h6 y9 ff4 fs4 fc0 sc0 ls0 ws0">Ab<span class="_ _6"></span>out<span class="_ _c"> </span>the<span class="_ _c"> </span>Compiler</div><div class="t m0 x6 h6 ya ff4 fs4 fc0 sc0 ls0 ws0">Compiler<span class="_ _c"> </span>Optimization<span class="_ _d"> </span>Flags</div><div class="t m0 x6 h6 yb ff4 fs4 fc0 sc0 ls0 ws0">Floating-p<span class="_ _6"></span>oint<span class="_ _c"> </span>Optimization<span class="_ _c"> </span>Flags</div><div class="t m0 x6 h6 yc ff4 fs4 fc0 sc0 ls0 ws0">Link<span class="_ _3"></span>er<span class="_ _d"> </span>Optimization<span class="_ _c"> </span>Flags</div><div class="t m0 x6 h6 yd ff4 fs4 fc0 sc0 ls0 ws0">Architecture<span class="_ _c"> </span>Flags</div><div class="t m0 x6 h6 ye ff4 fs4 fc0 sc0 ls0 ws0">Help<span class="_ _c"> </span>the<span class="_ _d"> </span>Compiler<span class="_ _c"> </span>to<span class="_ _d"> </span>Pro<span class="_ _6"></span>duce<span class="_ _c"> </span>Better<span class="_ _c"> </span>Co<span class="_ _6"></span>de</div><div class="t m0 x6 h6 yf ff4 fs4 fc0 sc0 ls0 ws0">Profile<span class="_ _c"> </span>Guided<span class="_ _d"> </span>Optimization<span class="_ _c"> </span>(PGO)</div><div class="t m0 x6 h6 y10 ff4 fs4 fc0 sc0 ls0 ws0">P<span class="_ _3"></span>ost-Pro<span class="_ _6"></span>cessing<span class="_ _c"> </span>Binary<span class="_ _c"> </span>Optimizer</div><div class="t m0 x7 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">1/76</div><a class="l" href="#pf7" data-dest-detail='[7,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:47.076000px;bottom:183.866000px;width:156.545000px;height:14.745000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfb" data-dest-detail='[11,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:167.309000px;width:84.600000px;height:10.848000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pfd" data-dest-detail='[13,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:149.899000px;width:121.254000px;height:10.848000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pff" data-dest-detail='[15,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:132.489000px;width:143.033000px;height:10.849000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf11" data-dest-detail='[17,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:115.080000px;width:109.603000px;height:10.848000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf12" data-dest-detail='[18,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:97.670000px;width:77.986000px;height:10.848000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf15" data-dest-detail='[21,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:80.260000px;width:181.154000px;height:10.849000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf16" data-dest-detail='[22,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:62.297000px;width:149.814000px;height:11.955000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf19" data-dest-detail='[25,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:45.441000px;width:142.065000px;height:10.848000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf3" class="pf w0 h0" data-page-no="3"><div class="pc pc3 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">T<span class="_ _8"></span>able<span class="_ _9"> </span>of<span class="_ _a"> </span>Contents</div><div class="t m0 x5 h7 y12 ff1 fs4 fc2 sc0 ls0 ws0">2<span class="_ _b"> </span><span class="fs2 fc0">Compiler<span class="_ _7"> </span>T<span class="_ _5"></span>ransfo<span class="_ _3"></span>rmation<span class="_ _7"> </span>T<span class="_ _5"></span>echniques</span></div><div class="t m0 x6 h6 y13 ff4 fs4 fc0 sc0 ls0 ws0">Basic<span class="_ _c"> </span>Compiler<span class="_ _d"> </span>T<span class="_ _8"></span>ransformations</div><div class="t m0 x6 h6 y14 ff4 fs4 fc0 sc0 ls0 ws0">Lo<span class="_ _6"></span>op<span class="_ _c"> </span>Unswitching</div><div class="t m0 x6 h6 y15 ff4 fs4 fc0 sc0 ls0 ws0">Lo<span class="_ _6"></span>op<span class="_ _c"> </span>F<span class="_ _3"></span>usion</div><div class="t m0 x6 h6 y16 ff4 fs4 fc0 sc0 ls0 ws0">Lo<span class="_ _6"></span>op<span class="_ _c"> </span>Fission</div><div class="t m0 x6 h6 y17 ff4 fs4 fc0 sc0 ls0 ws0">Lo<span class="_ _6"></span>op<span class="_ _c"> </span>Interchange</div><div class="t m0 x6 h6 y18 ff4 fs4 fc0 sc0 ls0 ws0">Lo<span class="_ _6"></span>op<span class="_ _c"> </span>Tiling</div><div class="t m0 x5 h7 y19 ff1 fs4 fc2 sc0 ls0 ws0">3<span class="_ _b"> </span><span class="fs2 fc0">Lib<span class="_ _3"></span>ra<span class="_ _3"></span>ries<span class="_ _7"> </span>and<span class="_ _e"> </span>Data<span class="_ _e"> </span>Structures</span></div><div class="t m0 x6 h6 y1a ff4 fs4 fc0 sc0 ls0 ws0">External<span class="_ _c"> </span>Libra<span class="_ _3"></span>ries</div><div class="t m0 x7 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">2/76</div><a class="l" href="#pf1c" data-dest-detail='[28,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:47.076000px;bottom:200.361000px;width:242.931000px;height:14.745000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf1e" data-dest-detail='[30,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:182.010000px;width:134.579000px;height:10.849000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf21" data-dest-detail='[33,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:162.807000px;width:76.781000px;height:10.849000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf22" data-dest-detail='[34,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:143.605000px;width:52.968000px;height:10.848000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf23" data-dest-detail='[35,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:124.402000px;width:54.296000px;height:10.848000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf24" data-dest-detail='[36,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:105.199000px;width:74.360000px;height:10.848000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf25" data-dest-detail='[37,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:85.996000px;width:50.145000px;height:10.848000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf26" data-dest-detail='[38,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:47.076000px;bottom:53.297000px;width:195.629000px;height:13.781000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf27" data-dest-detail='[39,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:35.920000px;width:74.194000px;height:8.911000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf4" class="pf w0 h0" data-page-no="4"><div class="pc pc4 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA4wAAAH/CAIAAACTgcSzAAAACXBIWXMAABYlAAAWJQFJUiTwAAAJNklEQVR42u3YMarVQBSA4TcyB0vXIFazAhFJkUJwZ27CvaSIhTtI4w6sXulprp1dxMsD51zyfe0Qwhym+Dnt3fuPTwAAUMaP799emQIAANWIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAQKQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAA/miZaQoAANQRETapAACUI1IBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAQKQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAwFz90S8wlvUlnx/75hEAAFRjkwoAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAARCoAACIVAABepmWmKQAAUEdE2KQCAFCOSAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAAc/XL3nws69nRsW9eBgDARDapAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAmK1lpikAAFBHRNikAgBQjkgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAABEKgAAIhUAAO7SL3vzsaxnR8e+eRkAABPZpAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAGC2lpmmAABAHRFhkwoAQDkiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAAEKkAAIhUAAAQqQAAPLZ+2ZuPZT07OvbNywAAmMgmFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAALO1zDQFAADqiAibVAAAyhGpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAECkAgCASAUAQKQCAMBd+mVvPpb17OjYNy8DAGAim1QAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAADM1jLTFAAAqCMibFIBAChHpAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAACIVAACRCgAAd+mXvflY1rOjY9+8DACAiWxSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAMFvLTFMAAKCOiLBJBQCgHJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAACIVAAARCoAAIhUAAAeW7/szceynh0d++ZlAABMZJMKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAIBIBQCA2VpmmgIAAHVEhE0qAADliFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBAECkAgAgUgEA4C79sjcfy3p2dOyblwEAMJFNKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAGZrmWkKAADUERE2qQAAlCNSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQAAkQoAgEgFAACRCgDAY+uXvflY1rOjY9+8DACAiWxSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAMFvLTFMAAKCOiLBJBQCgHJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAACIVAAARCoAANylX/bmY1nPjo598zIAACaySQUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAMBsLTNNAQCAOiLCJhUAgHJEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAQKQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgAAIhUAAJEKAAD/rD9/efvffvbz01cTBwDg78aHz629eX17/vX0dLvdDAQAgBJ+A8heT/DdWK0zAAAAAElFTkSuQmCC"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">T<span class="_ _8"></span>able<span class="_ _9"> </span>of<span class="_ _a"> </span>Contents</div><div class="t m0 x5 h7 y1b ff1 fs4 fc2 sc0 ls0 ws0">4<span class="_ _b"> </span><span class="fs2 fc0">P<span class="_ _3"></span>erfo<span class="_ _3"></span>rmance<span class="_ _e"> </span>Benchma<span class="_ _3"></span>rking</span></div><div class="t m0 x6 h6 y1c ff4 fs4 fc0 sc0 ls0 ws0">What<span class="_ _c"> </span>to<span class="_ _d"> </span>T<span class="_ _8"></span>est?</div><div class="t m0 x6 h6 y1d ff4 fs4 fc0 sc0 ls0 ws0">W<span class="_ _3"></span>orkload/Dataset<span class="_ _c"> </span>Quality</div><div class="t m0 x6 h6 y1e ff4 fs4 fc0 sc0 ls0 ws0">Cache<span class="_ _c"> </span>Behavior</div><div class="t m0 x6 h6 y1f ff4 fs4 fc0 sc0 ls0 ws0">Stable<span class="_ _c"> </span>CPU<span class="_ _d"> </span>P<span class="_ _3"></span>erformance</div><div class="t m0 x6 h6 y20 ff4 fs4 fc0 sc0 ls0 ws0">Multi-Threads<span class="_ _c"> </span>Considerations</div><div class="t m0 x6 h6 y21 ff4 fs4 fc0 sc0 ls0 ws0">Program<span class="_ _c"> </span>Memory<span class="_ _c"> </span>Lay<span class="_ _3"></span>out</div><div class="t m0 x6 h6 y22 ff4 fs4 fc0 sc0 ls0 ws0">Measurement<span class="_ _c"> </span>Overhead</div><div class="t m0 x6 h6 y23 ff4 fs4 fc0 sc0 ls0 ws0">Compiler<span class="_ _c"> </span>Optimizations</div><div class="t m0 x6 h6 y24 ff4 fs4 fc0 sc0 ls0 ws0">Metric<span class="_ _c"> </span>Evaluation</div><div class="t m0 x7 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">3/76</div><a class="l" href="#pf2b" data-dest-detail='[43,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:47.076000px;bottom:200.170000px;width:182.595000px;height:14.745000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf2d" data-dest-detail='[45,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:183.757000px;width:62.652000px;height:8.911000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf2e" data-dest-detail='[46,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:162.063000px;width:111.857000px;height:11.955000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf2f" data-dest-detail='[47,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:145.351000px;width:66.556000px;height:8.911000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf31" data-dest-detail='[49,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:126.148000px;width:105.867000px;height:8.911000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf35" data-dest-detail='[53,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:106.945000px;width:124.754000px;height:8.911000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf36" data-dest-detail='[54,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:85.805000px;width:106.185000px;height:10.848000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf37" data-dest-detail='[55,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:68.539000px;width:100.401000px;height:8.911000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf38" data-dest-detail='[56,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:47.399000px;width:100.111000px;height:10.848000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf3a" data-dest-detail='[58,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:30.133000px;width:76.020000px;height:8.911000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf5" class="pf w0 h0" data-page-no="5"><div class="pc pc5 w0 h0"><img class="bi x0 y0 w1 h1" alt="" 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class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">T<span class="_ _8"></span>able<span class="_ _9"> </span>of<span class="_ _a"> </span>Contents</div><div class="t m0 x5 h7 y25 ff1 fs4 fc2 sc0 ls0 ws0">5<span class="_ _b"> </span><span class="fs2 fc0">Profiling</span></div><div class="t m0 x6 h9 y26 ff6 fs4 fc0 sc0 ls0 ws0">gprof</div><div class="t m0 x6 h9 y27 ff6 fs4 fc0 sc0 ls0 ws0">uftrace</div><div class="t m0 x6 h9 y28 ff6 fs4 fc0 sc0 ls0 ws0">callgrind</div><div class="t m0 x6 h9 y29 ff6 fs4 fc0 sc0 ls0 ws0">cachegrind</div><div class="t m0 x6 h6 y2a ff6 fs4 fc0 sc0 ls0 ws0">perf<span class="_ _c"> </span><span class="ff4">Linux<span class="_ _d"> </span>p<span class="_ _3"></span>rofiler</span></div><div class="t m0 x7 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">4/76</div><a class="l" href="#pf40" data-dest-detail='[64,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:47.076000px;bottom:158.360000px;width:56.986000px;height:14.745000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf42" data-dest-detail='[66,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:141.582000px;width:28.144000px;height:10.328000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf44" data-dest-detail='[68,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:126.330000px;width:38.605000px;height:8.170000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf45" data-dest-detail='[69,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:106.763000px;width:49.066000px;height:10.327000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf46" data-dest-detail='[70,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:89.353000px;width:54.296000px;height:10.327000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf49" data-dest-detail='[73,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:71.943000px;width:81.029000px;height:11.069000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf6" class="pf w0 h0" data-page-no="6"><div class="pc pc6 w0 h0"><img class="bi x0 y0 w1 h1" alt="" 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class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">T<span class="_ _8"></span>able<span class="_ _9"> </span>of<span class="_ _a"> </span>Contents</div><div class="t m0 x5 h7 ya ff1 fs4 fc2 sc0 ls0 ws0">6<span class="_ _b"> </span><span class="fs2 fc0">P<span class="_ _3"></span>a<span class="_ _3"></span>rallel<span class="_ _e"> </span>Computing</span></div><div class="t m0 x6 h6 yb ff4 fs4 fc0 sc0 ls0 ws0">Concurrency<span class="_ _c"> </span>vs.<span class="_ _a"> </span>P<span class="_ _3"></span>arallelism</div><div class="t m0 x6 h6 yc ff4 fs4 fc0 sc0 ls0 ws0">P<span class="_ _3"></span>erformance<span class="_ _c"> </span>Scaling</div><div class="t m0 x6 h6 yd ff4 fs4 fc0 sc0 ls0 ws0">Gustafson’s<span class="_ _c"> </span>Law</div><div class="t m0 x6 h6 ye ff4 fs4 fc0 sc0 ls0 ws0">P<span class="_ _3"></span>arallel<span class="_ _c"> </span>Programming<span class="_ _d"> </span>Languages</div><div class="t m0 x7 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">5/76</div><a class="l" href="#pf4c" data-dest-detail='[76,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:47.076000px;bottom:149.047000px;width:127.103000px;height:14.745000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf4d" data-dest-detail='[77,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:132.489000px;width:116.868000px;height:10.849000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf4e" data-dest-detail='[78,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:115.080000px;width:86.730000px;height:10.848000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf4f" data-dest-detail='[79,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:99.607000px;width:69.599000px;height:8.911000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf50" data-dest-detail='[80,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:49.490000px;bottom:80.260000px;width:138.730000px;height:10.849000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf7" class="pf w0 h0" data-page-no="7"><div class="pc pc7 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x8 h2 y2b ff1 fs0 fc0 sc0 ls0 ws0">Compiler</div><div class="t m0 x8 h2 y2c ff1 fs0 fc0 sc0 ls0 ws0">Optimizations</div><a class="l" href="#pf7" data-dest-detail='[7,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:105.775000px;bottom:149.618000px;width:241.993000px;height:24.025000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf7" data-dest-detail='[7,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:105.775000px;bottom:115.247000px;width:158.930000px;height:24.025000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf8" class="pf w0 h0" data-page-no="8"><div class="pc pc8 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Ab<span class="_ _6"></span>out<span class="_ _9"> </span>Compiler<span class="_ _a"> </span>Optimizations<span class="_ _f"> </span>1/3</div><div class="t m0 x9 h5 y2d ff3 fs3 fc0 sc0 ls0 ws0">”I<span class="_ _c"> </span>alwa<span class="_ _3"></span>ys<span class="_ _d"> </span>sa<span class="_ _3"></span>y<span class="_ _d"> </span>the<span class="_ _c"> </span>purp<span class="_ _6"></span>ose<span class="_ _d"> </span>of<span class="_ _c"> </span>optimizing<span class="_ _d"> </span>compilers<span class="_ _d"> </span>is<span class="_ _c"> </span>not<span class="_ _d"> </span>to<span class="_ _d"> </span>mak<span class="_ _3"></span>e<span class="_ _d"> </span>co<span class="_ _6"></span>de</div><div class="t m0 x6 h5 y2e ff3 fs3 fc0 sc0 ls0 ws0">run<span class="_ _e"> </span>faster,<span class="_ _e"> </span>but<span class="_ _a"> </span>to<span class="_ _e"> </span>p<span class="_ _3"></span>revent<span class="_ _10"> </span>p<span class="_ _3"></span>rogrammers<span class="_ _10"> </span>from<span class="_ _10"> </span>writing<span class="_ _10"> </span>utter<span class="_ _10"> </span>****<span class="_ _10"> </span>in<span class="_ _10"> </span>the</div><div class="t m0 x6 h5 y2f ff3 fs3 fc0 sc0 ls0 ws0">pursuit<span class="_ _9"> </span>of<span class="_ _11"> </span>making<span class="_ _11"> </span>it<span class="_ _9"> </span>run<span class="_ _11"> </span>faster“</div><div class="t m0 xa h5 y30 ff7 fs3 fc0 sc0 ls0 ws0">Rich<span class="_ _a"> </span>F<span class="_ _3"></span>elk<span class="_ _3"></span>er<span class="ff3">,<span class="_ _11"> </span><span class="ff8">musl-libc<span class="_ _9"> </span></span>(<span class="ff8">libc<span class="_ _11"> </span></span>alternative)</span></div><div class="t m0 x7 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">6/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf9" class="pf w0 h0" data-page-no="9"><div class="pc pc9 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Ab<span class="_ _6"></span>out<span class="_ _9"> </span>Compiler<span class="_ _a"> </span>Optimizations<span class="_ _f"> </span>2/3</div><div class="t m0 xb ha y31 ff6 fs6 fc3 sc0 ls0 ws0">bool<span class="_ _a"> </span><span class="ff9 fc4">isEven<span class="fc0">(</span></span>int<span class="_ _a"> </span><span class="ff9 fc0">number)<span class="_ _10"> </span>{</span></div><div class="t m0 x6 ha y32 ff6 fs6 fc3 sc0 ls0 ws0">int<span class="_ _12"> </span><span class="ff9 fc0">numberCompare<span class="_ _a"> </span><span class="fc5">=<span class="_ _a"> </span>0</span>;</span></div><div class="t m0 x6 ha y33 ff6 fs6 fc3 sc0 ls0 ws0">bool<span class="_ _a"> </span><span class="ff9 fc0">even<span class="_ _13"> </span><span class="fc5">=<span class="_ _a"> </span><span class="fc6">true</span></span>;</span></div><div class="t m0 x6 ha y34 ff6 fs6 fc6 sc0 ls0 ws0">while<span class="_ _a"> </span><span class="ff9 fc0">(number<span class="_ _a"> </span><span class="fc5">!=<span class="_ _10"> </span></span>numberCompare)<span class="_ _a"> </span>{</span></div><div class="t m0 xc ha y35 ff9 fs6 fc0 sc0 ls0 ws0">even<span class="_ _a"> </span><span class="fc5">=<span class="_ _a"> </span>!</span>even;</div><div class="t m0 xc ha y36 ff9 fs6 fc0 sc0 ls0 ws0">numberCompare<span class="fc5">++</span>;</div><div class="t m0 x6 ha y37 ff9 fs6 fc0 sc0 ls0 ws0">}</div><div class="t m0 x6 ha y38 ff6 fs6 fc6 sc0 ls0 ws0">return<span class="_ _a"> </span><span class="ff9 fc0">even;</span></div><div class="t m0 xb ha y39 ff9 fs6 fc0 sc0 ls0 ws0">}</div><div class="t m0 xd hb y3a ffa fs1 fc0 sc0 ls0 ws0">→</div><div class="t m0 xe ha y3b ff6 fs6 fc3 sc0 ls0 ws0">bool<span class="_ _a"> </span><span class="ff9 fc4">isEven<span class="fc0">(</span></span>int<span class="_ _a"> </span><span class="ff9 fc0">number)<span class="_ _10"> </span>{</span></div><div class="t m0 xf ha y3c ff6 fs6 fc6 sc0 ls0 ws0">return<span class="_ _a"> </span><span class="ff9 fc0">number<span class="_ _a"> </span><span class="fc5">&<span class="_ _10"> </span>1u</span>;</span></div><div class="t m0 xe ha y3d ff9 fs6 fc0 sc0 ls0 ws0">}</div><div class="t m0 x10 ha y3e ff9 fs6 fc7 sc0 ls0 ws0">Exploring<span class="_ _a"> </span>Clang/LLVM<span class="_ _a"> </span>optimization<span class="_ _10"> </span>on<span class="_ _a"> </span>programming<span class="_ _10"> </span>horror</div><div class="t m0 x7 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">7/76</div><a class="l" href="https://blog.matthieud.me/2020/exploring-clang-llvm-optimization-on-programming-horror/"><div class="d m1" style="border-style:none;position:absolute;left:34.722000px;bottom:8.863000px;width:260.897000px;height:13.445000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
|
||
<div id="pfa" class="pf w0 h0" data-page-no="a"><div class="pc pca w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Ab<span class="_ _6"></span>out<span class="_ _9"> </span>Compiler<span class="_ _a"> </span>Optimizations<span class="_ _f"> </span>3/3</div><div class="t m0 x1 hc y3f ff4 fs7 fc0 sc0 ls0 ws0">On<span class="_ _d"> </span>the<span class="_ _11"> </span>other<span class="_ _d"> </span>hand,<span class="_ _11"> </span>having<span class="_ _d"> </span>a<span class="_ _11"> </span>go<span class="_ _6"></span>od<span class="_ _11"> </span>compiler<span class="_ _11"> </span>do<span class="_ _6"></span>es<span class="_ _d"> </span>not<span class="_ _d"> </span>mean<span class="_ _11"> </span>that<span class="_ _d"> </span>it<span class="_ _11"> </span>can<span class="_ _d"> </span>fully<span class="_ _11"> </span>optimize</div><div class="t m0 x1 hc y40 ff4 fs7 fc0 sc0 ls0 ws0">any<span class="_ _d"> </span>co<span class="_ _6"></span>de:</div><div class="t m0 x10 hc y41 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">The<span class="_ _d"> </span>compiler<span class="_ _11"> </span>do<span class="_ _6"></span>es<span class="_ _d"> </span>not<span class="_ _d"> </span><span class="ffc">“understand”<span class="_ _a"> </span></span>the<span class="_ _d"> </span>co<span class="_ _6"></span>de,<span class="_ _d"> </span>as<span class="_ _11"> </span>opp<span class="_ _6"></span>osed<span class="_ _d"> </span>to<span class="_ _11"> </span>human</span></div><div class="t m0 x10 hc y42 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">The<span class="_ _d"> </span>compiler<span class="_ _d"> </span>is<span class="_ _d"> </span><span class="ffc">conservative<span class="_ _9"> </span></span>and<span class="_ _d"> </span>applies<span class="_ _d"> </span>optimizations<span class="_ _d"> </span>only<span class="_ _d"> </span>if<span class="_ _c"> </span>they<span class="_ _d"> </span>are<span class="_ _c"> </span>safe<span class="_ _d"> </span>and<span class="_ _d"> </span>do</span></div><div class="t m0 x6 hc y43 ff4 fs7 fc0 sc0 ls0 ws0">not<span class="_ _d"> </span>affect<span class="_ _11"> </span>the<span class="_ _d"> </span>correctness<span class="_ _d"> </span>of<span class="_ _d"> </span>computation</div><div class="t m0 x10 hc y44 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">The<span class="_ _d"> </span>compiler<span class="_ _11"> </span>is<span class="_ _d"> </span>full<span class="_ _11"> </span>of<span class="_ _d"> </span><span class="ffc">mo<span class="_ _6"></span>dels<span class="_ _d"> </span>and<span class="_ _11"> </span>heuristics<span class="_ _a"> </span></span>that<span class="_ _d"> </span>could<span class="_ _11"> </span>not<span class="_ _d"> </span>match<span class="_ _11"> </span>a<span class="_ _d"> </span>sp<span class="_ _6"></span>ecific</span></div><div class="t m0 x6 hc y45 ff4 fs7 fc0 sc0 ls0 ws0">situation</div><div class="t m0 x10 hc y46 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">The<span class="_ _d"> </span>compiler<span class="_ _11"> </span><span class="ffc">cannot<span class="_ _d"> </span>sp<span class="_ _6"></span>end<span class="_ _d"> </span>large<span class="_ _d"> </span>amount<span class="_ _d"> </span>of<span class="_ _11"> </span>time<span class="_ _9"> </span></span>in<span class="_ _11"> </span>co<span class="_ _6"></span>de<span class="_ _d"> </span>optimization</span></div><div class="t m0 x10 hc y47 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">The<span class="_ _d"> </span>compiler<span class="_ _11"> </span>could<span class="_ _d"> </span>consider<span class="_ _11"> </span><span class="ffc">other<span class="_ _d"> </span>targets<span class="_ _a"> </span></span>outside<span class="_ _d"> </span>p<span class="_ _6"></span>erfo<span class="_ _3"></span>rmance,<span class="_ _d"> </span>e.g.<span class="_ _10"> </span>binary<span class="_ _d"> </span>size</span></div><div class="t m0 x7 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">8/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
|
||
<div id="pfb" class="pf w0 h0" data-page-no="b"><div class="pc pcb w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Ab<span class="_ _6"></span>out<span class="_ _9"> </span>the<span class="_ _a"> </span>Compiler<span class="_ _14"> </span>1/2</div><div class="t m0 x1 hc y48 ffc fs7 fc0 sc0 ls0 ws0">Imp<span class="_ _6"></span>o<span class="_ _3"></span>rtant<span class="_ _d"> </span>advise:<span class="_ _e"> </span><span class="ff1 fc8">Use<span class="_ _9"> </span>an<span class="_ _9"> </span>up<span class="_ _6"></span>dated<span class="_ _11"> </span>version<span class="_ _9"> </span>of<span class="_ _9"> </span>the<span class="_ _9"> </span>compiler</span></div><div class="t m0 x10 hc y49 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">New<span class="_ _3"></span>er<span class="_ _11"> </span>compiler<span class="_ _d"> </span>produces<span class="_ _11"> </span><span class="ff1">b<span class="_ _6"></span>etter/faster<span class="_ _9"> </span>co<span class="_ _6"></span>de</span></span></div><div class="t m0 x11 h6 y4a ff4 fs4 fc0 sc0 ls0 ws0">-<span class="_ _7"> </span>Effective<span class="_ _c"> </span>optimizations</div><div class="t m0 x11 h6 y4b ff4 fs4 fc0 sc0 ls0 ws0">-<span class="_ _7"> </span>Supp<span class="_ _6"></span>o<span class="_ _3"></span>rt<span class="_ _c"> </span>for<span class="_ _c"> </span>newer<span class="_ _c"> </span>CPU<span class="_ _c"> </span>architectures</div><div class="t m0 x10 hc y4c ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">New<span class="_ _9"> </span>w<span class="_ _3"></span>arnings<span class="_ _d"> </span><span class="ff4">to<span class="_ _d"> </span>avoid<span class="_ _11"> </span>common<span class="_ _d"> </span>errors<span class="_ _d"> </span>and<span class="_ _d"> </span>b<span class="_ _6"></span>etter<span class="_ _d"> </span>supp<span class="_ _6"></span>o<span class="_ _3"></span>rt<span class="_ _11"> </span>fo<span class="_ _3"></span>r<span class="_ _11"> </span>existing</span></span></div><div class="t m0 x6 hc y4d ff4 fs7 fc0 sc0 ls0 ws0">erro<span class="_ _3"></span>r/wa<span class="_ _3"></span>rnings<span class="_ _d"> </span>(e.g.<span class="_ _10"> </span>co<span class="_ _6"></span>de<span class="_ _d"> </span>highlights)</div><div class="t m0 x10 hc y4e ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">F<span class="_ _3"></span>aster<span class="_ _9"> </span>compiling,<span class="_ _9"> </span>less<span class="_ _9"> </span>memo<span class="_ _3"></span>ry<span class="_ _9"> </span>usage</span></div><div class="t m0 x10 hc y4f ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Less<span class="_ _9"> </span>compiler<span class="_ _11"> </span>bugs<span class="ff4">:<span class="_ _10"> </span>compilers<span class="_ _11"> </span>are<span class="_ _d"> </span>very<span class="_ _d"> </span>complex<span class="_ _d"> </span>and<span class="_ _11"> </span>they<span class="_ _d"> </span>have<span class="_ _11"> </span>many<span class="_ _d"> </span>bugs</span></span></div><div class="t m0 x1 hc y50 ff7 fs7 fc0 sc0 ls0 ws0">Use<span class="_ _11"> </span>an<span class="_ _9"> </span>up<span class="_ _6"></span>dated<span class="_ _9"> </span>version<span class="_ _9"> </span>of<span class="_ _11"> </span>the<span class="_ _9"> </span>linker<span class="_ _15"></span><span class="ff4">:<span class="_ _10"> </span>e.g.<span class="_ _a"> </span>for<span class="_ _d"> </span><span class="ffc">Link<span class="_ _d"> </span>Time<span class="_ _11"> </span>Optimization</span>,</span></div><div class="t m0 xb hc y51 ffd fs7 fc0 sc0 ls0 ws0">gold<span class="_ _16"> </span>linker<span class="_ _17"> </span><span class="ff4">o<span class="_ _3"></span>r<span class="_ _11"> </span>LL<span class="_ _5"></span>VM<span class="_ _11"> </span>linker<span class="_ _17"> </span><span class="ffd">lld</span></span></div><div class="t m0 x7 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">9/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pfc" class="pf w0 h0" data-page-no="c"><div class="pc pcc w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Ab<span class="_ _6"></span>out<span class="_ _9"> </span>the<span class="_ _a"> </span>Compiler<span class="_ _14"> </span>2/2</div><div class="t m0 x1 h5 y52 ff1 fs3 fc8 sc0 ls0 ws0">Which<span class="_ _a"> </span>compiler?</div><div class="t m0 x1 hc y53 ff1 fs7 fc0 sc0 ls0 ws0">Answ<span class="_ _3"></span>er:<span class="_ _10"> </span><span class="ff4">It<span class="_ _d"> </span>dep<span class="_ _6"></span>endents<span class="_ _d"> </span>on<span class="_ _11"> </span>the<span class="_ _11"> </span>co<span class="_ _6"></span>de<span class="_ _d"> </span>and<span class="_ _d"> </span>on<span class="_ _11"> </span>the<span class="_ _d"> </span>processor</span></div><div class="t m0 x1 hc y54 ff4 fs7 fc0 sc0 ls0 ws0">example:<span class="_ _10"> </span><span class="ffd">GCC<span class="_ _7"> </span>9<span class="_ _16"> </span>vs.<span class="_ _18"> </span>Clang<span class="_ _16"> </span>8</span></div><div class="t m0 x1 hc y55 ff4 fs7 fc0 sc0 ls0 ws0">Some<span class="_ _d"> </span>compilers<span class="_ _11"> </span>can<span class="_ _d"> </span>produce<span class="_ _11"> </span>optimized<span class="_ _d"> </span>co<span class="_ _6"></span>de<span class="_ _d"> </span>for<span class="_ _d"> </span>sp<span class="_ _6"></span>ecific<span class="_ _d"> </span>a<span class="_ _3"></span>rchitectures:</div><div class="t m0 x10 hc y56 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Intel<span class="_ _9"> </span>Compiler<span class="_ _11"> </span><span class="ff4">(commercial):<span class="_ _a"> </span>Intel<span class="_ _11"> </span>processors</span></span></div><div class="t m0 x10 hc y57 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">IBM<span class="_ _9"> </span>XL<span class="_ _11"> </span>Compiler<span class="_ _11"> </span><span class="ff4">(commercial):<span class="_ _10"> </span>IBM<span class="_ _11"> </span>processors/system</span></span></div><div class="t m0 x10 hc y58 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Nvidia<span class="_ _9"> </span>NV<span class="_ _3"></span>C++<span class="_ _9"> </span>Compiler<span class="_ _11"> </span><span class="ff4">(free/commercial):<span class="_ _10"> </span>Multi-co<span class="_ _3"></span>re<span class="_ _11"> </span>processors/GPUs</span></span></div><div class="t m0 x12 hd y59 ffe fs6 fc7 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff9">gcc.gnu.org/onlinedocs/gcc/Optimize-Options.html</span></div><div class="t m0 x12 hd y5a ffe fs6 fc7 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff9">Intel<span class="_ _a"> </span>Blog:<span class="_ _12"> </span>gcc-x86-performance-hints</span></div><div class="t m0 x12 hd y5b ffe fs6 fc7 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff9">Advanced<span class="_ _a"> </span>Optimization<span class="_ _10"> </span>and<span class="_ _a"> </span>New<span class="_ _a"> </span>Capa-bilities<span class="_ _10"> </span>of<span class="_ _a"> </span>GCC<span class="_ _a"> </span>10</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">10/76</div><a class="l" href="https://www.phoronix.com/scan.php?page=article&item=gcc9-clang8-hedt&num=1"><div class="d m1" style="border-style:none;position:absolute;left:72.108000px;bottom:157.173000px;width:105.083000px;height:11.993000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://gcc.gnu.org/onlinedocs/gcc/Optimize-Options.html"><div class="d m1" style="border-style:none;position:absolute;left:49.168000px;bottom:29.488000px;width:227.945000px;height:10.212000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://software.intel.com/en-us/blogs/2012/09/26/gcc-x86-performance-hints"><div class="d m1" style="border-style:none;position:absolute;left:49.168000px;bottom:16.886000px;width:180.872000px;height:9.365000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://documentation.suse.com/sbp/all/pdf/SBP-GCC-10_color_en.pdf"><div class="d m1" style="border-style:none;position:absolute;left:49.168000px;bottom:4.283000px;width:251.482000px;height:9.365000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pfd" class="pf w0 h0" data-page-no="d"><div class="pc pcd w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Compiler<span class="_ _a"> </span>Optimization<span class="_ _9"> </span>Flags<span class="_ _19"> </span>1/2</div><div class="t m0 x14 hc y5c ff6 fs7 fc0 sc0 ls0 ws0">-O0<span class="_ _c"> </span><span class="ff1">,<span class="_ _17"> </span></span>/Od<span class="_ _1a"> </span><span class="ff4">Disables<span class="_ _d"> </span>any<span class="_ _11"> </span>optimization</span></div><div class="t m0 x8 h6 y5d ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">default<span class="_ _c"> </span>b<span class="_ _6"></span>ehavior</span></div><div class="t m0 x8 h6 y5e ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">fast<span class="_ _c"> </span>compile<span class="_ _d"> </span>time</span></div><div class="t m0 x14 hc y5f ff6 fs7 fc0 sc0 ls0 ws0">-O1<span class="_ _c"> </span><span class="ff1">,<span class="_ _17"> </span></span>/O1<span class="_ _1a"> </span><span class="ff4">Enables<span class="_ _d"> </span>basic<span class="_ _11"> </span>optimizations</span></div><div class="t m0 x14 hc y60 ff6 fs7 fc0 sc0 ls0 ws0">-O2<span class="_ _c"> </span><span class="ff1">,<span class="_ _17"> </span></span>/O2<span class="_ _1a"> </span><span class="ff4">Enables<span class="_ _d"> </span>advanced<span class="_ _11"> </span>optimizations</span></div><div class="t m0 x8 h6 y61 ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">some<span class="_ _c"> </span>optimization<span class="_ _d"> </span>steps<span class="_ _c"> </span>are<span class="_ _c"> </span>exp<span class="_ _6"></span>ensive</span></div><div class="t m0 x8 h6 y62 ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">can<span class="_ _c"> </span>increase<span class="_ _d"> </span>the<span class="_ _c"> </span>binary<span class="_ _c"> </span>size</span></div><div class="t m0 x15 hc y63 ff6 fs7 fc0 sc0 ls0 ws0">-O3<span class="_ _1a"> </span><span class="ff4">Enable<span class="_ _d"> </span>aggressive<span class="_ _d"> </span>optimizations.<span class="_ _10"> </span>T<span class="_ _8"></span>urns<span class="_ _d"> </span>on<span class="_ _11"> </span>all<span class="_ _d"> </span>optimizations<span class="_ _11"> </span>sp<span class="_ _6"></span>ecified<span class="_ _d"> </span>by</span></div><div class="t m0 x16 hc y64 ffd fs7 fc0 sc0 ls0 ws0">-O2<span class="ff4">,<span class="_ _d"> </span>plus<span class="_ _11"> </span>some<span class="_ _d"> </span>more</span></div><div class="t m0 x8 h6 y65 ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _1a"> </span><span class="ffd">-O3<span class="_ _1b"> </span><span class="ff4">do<span class="_ _6"></span>es<span class="_ _c"> </span>not<span class="_ _c"> </span>guarantee<span class="_ _c"> </span>to<span class="_ _c"> </span>produce<span class="_ _d"> </span>faster<span class="_ _c"> </span>co<span class="_ _6"></span>de<span class="_ _c"> </span>than<span class="_ _17"> </span></span>-O2</span></div><div class="t m0 x8 h6 y66 ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">it<span class="_ _c"> </span>could<span class="_ _d"> </span>break<span class="_ _c"> </span>floating-point<span class="_ _d"> </span>IEEE754<span class="_ _c"> </span>rules<span class="_ _d"> </span>in<span class="_ _c"> </span>some<span class="_ _d"> </span>non-traditional</span></div><div class="t m0 x17 h6 y67 ff4 fs4 fc0 sc0 ls0 ws0">compilers<span class="_ _c"> </span>(<span class="ffd">nvc++</span>,<span class="_ _d"> </span><span class="ffd">IBM<span class="_ _e"> </span>xlc</span>)</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">11/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pfe" class="pf w0 h0" data-page-no="e"><div class="pc pce w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Compiler<span class="_ _a"> </span>Optimization<span class="_ _9"> </span>Flags<span class="_ _19"> </span>2/2</div><div class="t m0 x18 hc y5c ff6 fs7 fc0 sc0 ls0 ws0">-O4<span class="_ _c"> </span><span class="ff1">/<span class="_"> </span></span>-O5<span class="_ _1a"> </span><span class="ff4">It<span class="_ _d"> </span>is<span class="_ _d"> </span>an<span class="_ _11"> </span>alias<span class="_ _d"> </span>of<span class="_ _17"> </span><span class="ffd">-O3<span class="_ _17"> </span></span>in<span class="_ _11"> </span>some<span class="_ _d"> </span>compilers,<span class="_ _11"> </span>or<span class="_ _c"> </span>it<span class="_ _11"> </span>can<span class="_ _11"> </span>refer<span class="_ _d"> </span>to<span class="_ _17"> </span><span class="ffd">-O3<span class="_ _17"> </span></span>+</span></div><div class="t m0 x16 hc y68 ff4 fs7 fc0 sc0 ls0 ws0">inter-p<span class="_ _3"></span>ro<span class="_ _6"></span>cedural<span class="_ _d"> </span>optimizations<span class="_ _11"> </span>(basic,<span class="_ _d"> </span>full)<span class="_ _11"> </span>and<span class="_ _d"> </span>high-order</div><div class="t m0 x16 hc y69 ff4 fs7 fc0 sc0 ls0 ws0">transfo<span class="_ _3"></span>rmation<span class="_ _11"> </span>(HOT)<span class="_ _d"> </span>optimizer<span class="_ _11"> </span>for<span class="_ _d"> </span>specialized<span class="_ _11"> </span>lo<span class="_ _6"></span>op<span class="_ _d"> </span>transformations</div><div class="t m0 x19 hc y6a ff6 fs7 fc0 sc0 ls0 ws0">-Ofast<span class="_ _1a"> </span><span class="ff4">Provides<span class="_ _d"> </span>other<span class="_ _d"> </span>aggressive<span class="_ _d"> </span>optimizations<span class="_ _11"> </span>that<span class="_ _11"> </span>ma<span class="_ _3"></span>y<span class="_ _11"> </span>violate<span class="_ _d"> </span>strict</span></div><div class="t m0 x16 hc y6b ff4 fs7 fc0 sc0 ls0 ws0">compliance<span class="_ _d"> </span>with<span class="_ _11"> </span>language<span class="_ _d"> </span>standards.<span class="_ _a"> </span>It<span class="_ _11"> </span>includes<span class="_ _17"> </span><span class="ffd">-O3<span class="_ _16"> </span>-ffast-math</span></div><div class="t m0 x14 hc y6c ff6 fs7 fc0 sc0 ls0 ws0">-Os<span class="_ _c"> </span><span class="ff1">,<span class="_ _17"> </span></span>/Os<span class="_ _1a"> </span><span class="ff4">Optimize<span class="_ _d"> </span>for<span class="_ _d"> </span>size.<span class="_ _a"> </span>It<span class="_ _11"> </span>enables<span class="_ _11"> </span>all<span class="_ _17"> </span><span class="ffd">-O2<span class="_ _17"> </span></span>optimizations<span class="_ _d"> </span>that<span class="_ _11"> </span>do<span class="_ _d"> </span>not</span></div><div class="t m0 x16 hc y6d ff4 fs7 fc0 sc0 ls0 ws0">t<span class="_ _3"></span>ypically<span class="_ _11"> </span>increase<span class="_ _d"> </span>co<span class="_ _6"></span>de<span class="_ _d"> </span>size<span class="_ _11"> </span>(e.g.<span class="_ _10"> </span>lo<span class="_ _6"></span>op<span class="_ _d"> </span>unrolling)</div><div class="t m0 x15 hc y6e ff6 fs7 fc0 sc0 ls0 ws0">-Oz<span class="_ _1a"> </span><span class="ff4">Aggressively<span class="_ _d"> </span>optimize<span class="_ _d"> </span>for<span class="_ _d"> </span>size</span></div><div class="t m0 x1a hc y6f ff6 fs7 fc0 sc0 ls0 ws0">-funroll-loops<span class="_ _1a"> </span><span class="ff4">Enables<span class="_ _d"> </span>loop<span class="_ _11"> </span>unrolling<span class="_ _11"> </span>(not<span class="_ _d"> </span>included<span class="_ _11"> </span>in<span class="_ _17"> </span><span class="ffd">-O3<span class="_ _c"> </span></span>)</span></div><div class="t m0 x1b hc y70 ff6 fs7 fc0 sc0 ls0 ws0">-fopt-info<span class="_ _1a"> </span><span class="ff4">Describes<span class="_ _d"> </span>optimization<span class="_ _11"> </span>passes<span class="_ _11"> </span>and<span class="_ _d"> </span>missed<span class="_ _11"> </span>optimizations</span></div><div class="t m0 x1c he y71 ffd fs7 fc0 sc0 ls0 ws0">-fopt-info-missed</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">12/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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||
<div id="pff" class="pf w0 h0" data-page-no="f"><div class="pc pcf w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Floating-p<span class="_ _6"></span>oint<span class="_ _9"> </span>Optimization<span class="_ _a"> </span>Flags<span class="_ _1c"> </span>1/2</div><div class="t m0 x1 hc y48 ff4 fs7 fc0 sc0 ls0 ws0">In<span class="_ _d"> </span>general,<span class="_ _11"> </span>enabling<span class="_ _d"> </span>the<span class="_ _11"> </span>follo<span class="_ _3"></span>wing<span class="_ _11"> </span>flags<span class="_ _d"> </span>implies<span class="_ _11"> </span>less<span class="_ _11"> </span>floating-p<span class="_ _6"></span>oint<span class="_ _d"> </span>accuracy<span class="_ _8"></span>,<span class="_ _d"> </span>breaking</div><div class="t m0 x1 hc y72 ff4 fs7 fc0 sc0 ls0 ws0">the<span class="_ _d"> </span>IEEE754<span class="_ _11"> </span>standa<span class="_ _3"></span>rd,<span class="_ _11"> </span>and<span class="_ _d"> </span>it<span class="_ _11"> </span>is<span class="_ _d"> </span>implementation<span class="_ _11"> </span>dep<span class="_ _6"></span>endent<span class="_ _d"> </span>(not<span class="_ _11"> </span>included<span class="_ _d"> </span>in<span class="_ _17"> </span><span class="ffd">-O3<span class="_ _c"> </span></span>)</div><div class="t m0 x1d hf y73 ff6 fs7 fc0 sc0 ls0 ws0">-fno-signaling-nans</div><div class="t m0 x1e hc y74 ff6 fs7 fc0 sc0 ls0 ws0">-fno-trapping-math<span class="_ _1a"> </span><span class="ff4">Disable<span class="_ _d"> </span>floating-point<span class="_ _11"> </span>exceptions</span></div><div class="t m0 xb hc y75 ff6 fs7 fc0 sc0 ls0 ws0">-mfma<span class="_ _16"> </span>-ffp-contract=fast<span class="_ _1a"> </span><span class="ff4">F<span class="_ _3"></span>o<span class="_ _3"></span>rce<span class="_ _11"> </span>floating-p<span class="_ _6"></span>oint<span class="_ _d"> </span>exp<span class="_ _3"></span>ression<span class="_ _11"> </span>contraction<span class="_ _d"> </span>such<span class="_ _11"> </span>as</span></div><div class="t m0 x1f hc y76 ff4 fs7 fc0 sc0 ls0 ws0">fo<span class="_ _3"></span>rming<span class="_ _11"> </span>of<span class="_ _d"> </span>fused<span class="_ _11"> </span>multiply-add<span class="_ _d"> </span>op<span class="_ _6"></span>erations</div><div class="t m0 x1e hc y77 ff6 fs7 fc0 sc0 ls0 ws0">-ffinite-math-only<span class="_ _1a"> </span><span class="ff4">Disable<span class="_ _c"> </span>sp<span class="_ _6"></span>ecial<span class="_ _d"> </span>conditions<span class="_ _d"> </span>for<span class="_ _c"> </span>handling<span class="_ _17"> </span><span class="ffd">inf<span class="_ _17"> </span></span>and<span class="_ _17"> </span><span class="ffd">NaN</span></span></div><div class="t m0 x1e hc y78 ff6 fs7 fc0 sc0 ls0 ws0">-fassociative-math<span class="_ _1a"> </span><span class="ff4">Assume<span class="_ _d"> </span>floating-point<span class="_ _11"> </span>asso<span class="_ _6"></span>ciative<span class="_ _d"> </span>b<span class="_ _6"></span>ehavio<span class="_ _3"></span>r</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">13/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
|
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<div id="pf10" class="pf w0 h0" data-page-no="10"><div class="pc pc10 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Floating-p<span class="_ _6"></span>oint<span class="_ _9"> </span>Optimization<span class="_ _a"> </span>Flags<span class="_ _1c"> </span>2/2</div><div class="t m0 x20 hc y79 ff6 fs7 fc0 sc0 ls0 ws0">-funsafe-math-optimizations<span class="_ _1a"> </span><span class="ff4">Allo<span class="_ _3"></span>ws<span class="_ _d"> </span>breaking<span class="_ _d"> </span>floating-p<span class="_ _6"></span>oint<span class="_ _d"> </span>asso<span class="_ _6"></span>ciativit<span class="_ _3"></span>y<span class="_ _d"> </span>and</span></div><div class="t m0 x1f hc y7a ff4 fs7 fc0 sc0 ls0 ws0">enables<span class="_ _d"> </span>reciprocal<span class="_ _11"> </span>optimization</div><div class="t m0 x8 hc y7b ff6 fs7 fc0 sc0 ls0 ws0">-ffast-math<span class="_ _1a"> </span><span class="ff4">Enables<span class="_ _d"> </span>aggressive<span class="_ _d"> </span>floating-p<span class="_ _6"></span>oint<span class="_ _d"> </span>optimizations.<span class="_ _10"> </span>All</span></div><div class="t m0 x1f hc y7c ff4 fs7 fc0 sc0 ls0 ws0">the<span class="_ _d"> </span>previous,<span class="_ _d"> </span>flush-to-zero<span class="_ _d"> </span>denormal<span class="_ _d"> </span>numb<span class="_ _6"></span>er,<span class="_ _d"> </span>plus</div><div class="t m0 x1f hc y7d ff4 fs7 fc0 sc0 ls0 ws0">others</div><div class="t m0 x10 ha y7e ff9 fs6 fc7 sc0 ls0 ws0">Beware<span class="_ _a"> </span>of<span class="_ _a"> </span>fast-math</div><div class="t m0 x5 ha y7f ff9 fs6 fc7 sc0 ls0 ws0">Semantics<span class="_ _a"> </span>of<span class="_ _a"> </span>Floating<span class="_ _10"> </span>Point<span class="_ _a"> </span>Math<span class="_ _10"> </span>in<span class="_ _a"> </span>GCC</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">14/76</div><a class="l" href="https://simonbyrne.github.io/notes/fastmath/"><div class="d m1" style="border-style:none;position:absolute;left:34.722000px;bottom:17.625000px;width:91.432000px;height:9.664000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://gcc.gnu.org/wiki/FloatingPointMath"><div class="d m1" style="border-style:none;position:absolute;left:34.324000px;bottom:1.241000px;width:185.579000px;height:11.154000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf11" class="pf w0 h0" data-page-no="11"><div class="pc pc11 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Link<span class="_ _3"></span>er<span class="_ _a"> </span>Optimization<span class="_ _9"> </span>Flags</div><div class="t m0 x1e hc y5c ff6 fs7 fc0 sc0 ls0 ws0">-flto<span class="_ _1a"> </span><span class="ff4">Enables<span class="_ _d"> </span><span class="ffc">Link<span class="_ _d"> </span>Time<span class="_ _d"> </span>Optimizations<span class="_ _a"> </span></span>(Interprocedural<span class="_ _11"> </span>Optimization).<span class="_ _10"> </span>The</span></div><div class="t m0 x21 hc y68 ff4 fs7 fc0 sc0 ls0 ws0">link<span class="_ _3"></span>er<span class="_ _11"> </span>merges<span class="_ _d"> </span>all<span class="_ _11"> </span>mo<span class="_ _6"></span>dules<span class="_ _d"> </span>into<span class="_ _d"> </span>a<span class="_ _11"> </span>single<span class="_ _d"> </span>combined<span class="_ _11"> </span>mo<span class="_ _6"></span>dule<span class="_ _d"> </span>for</div><div class="t m0 x21 hc y69 ff4 fs7 fc0 sc0 ls0 ws0">optimization</div><div class="t m0 x22 h6 y80 ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">the<span class="_ _c"> </span>linker<span class="_ _c"> </span>must<span class="_ _d"> </span>support<span class="_ _c"> </span>this<span class="_ _d"> </span>feature:<span class="_ _a"> </span><span class="ffd">GNU<span class="_ _10"> </span>ld<span class="_ _e"> </span>v2.21++<span class="_ _d"> </span></span>o<span class="_ _3"></span>r<span class="_ _d"> </span><span class="ffd">gold<span class="_ _d"> </span></span>version,</span></div><div class="t m0 x23 h6 y81 ff4 fs4 fc0 sc0 ls0 ws0">to<span class="_ _c"> </span>check<span class="_ _d"> </span>with<span class="_ _1b"> </span><span class="ffd">ld<span class="_ _7"> </span>--version</span></div><div class="t m0 x22 h6 y82 ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">it<span class="_ _c"> </span>can<span class="_ _d"> </span>significantly<span class="_ _c"> </span>improve<span class="_ _c"> </span>the<span class="_ _d"> </span>performance</span></div><div class="t m0 x22 h6 y83 ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">in<span class="_ _c"> </span>general,<span class="_ _d"> </span>it<span class="_ _c"> </span>is<span class="_ _d"> </span>a<span class="_ _c"> </span>very<span class="_ _d"> </span>exp<span class="_ _6"></span>ensive<span class="_ _c"> </span>step,<span class="_ _c"> </span>even<span class="_ _d"> </span>longer<span class="_ _c"> </span>than<span class="_ _d"> </span>the<span class="_ _c"> </span>object</span></div><div class="t m0 x23 h6 y84 ff4 fs4 fc0 sc0 ls0 ws0">compilations</div><div class="t m0 x1a hc y85 ff6 fs7 fc0 sc0 ls0 ws0">-fwhole-program<span class="_ _1a"> </span><span class="ff4">Assume<span class="_ _d"> </span>that<span class="_ _d"> </span>the<span class="_ _d"> </span>current<span class="_ _11"> </span>compilation<span class="_ _11"> </span>unit<span class="_ _d"> </span>represents<span class="_ _d"> </span>the<span class="_ _d"> </span>whole</span></div><div class="t m0 x21 hc y86 ff4 fs7 fc0 sc0 ls0 ws0">p<span class="_ _3"></span>rogram<span class="_ _11"> </span>b<span class="_ _6"></span>eing<span class="_ _d"> </span>compiled<span class="_ _d"> </span><span class="ffa">→<span class="_ _11"> </span></span>Assume<span class="_ _d"> </span>that<span class="_ _11"> </span>all<span class="_ _d"> </span>non-extern<span class="_ _11"> </span>functions<span class="_ _11"> </span>and</div><div class="t m0 x21 hc y87 ff4 fs7 fc0 sc0 ls0 ws0">va<span class="_ _3"></span>riables<span class="_ _11"> </span>b<span class="_ _6"></span>elong<span class="_ _d"> </span>only<span class="_ _d"> </span>to<span class="_ _11"> </span>their<span class="_ _d"> </span>compilation<span class="_ _11"> </span>unit</div><div class="t m0 x10 ha y88 ff9 fs6 fc7 sc0 ls0 ws0">Ubuntu<span class="_ _a"> </span>21.04<span class="_ _a"> </span>To<span class="_ _10"> </span>Turn<span class="_ _a"> </span>On<span class="_ _10"> </span>LTO<span class="_ _a"> </span>Optimizations<span class="_ _a"> </span>For<span class="_ _10"> </span>Its<span class="_ _a"> </span>Packages</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">15/76</div><a class="l" href="https://www.phoronix.com/scan.php?page=news_item&px=Ubuntu-21.04-LTO-Packages"><div class="d m1" style="border-style:none;position:absolute;left:34.722000px;bottom:8.097000px;width:275.019000px;height:13.445000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf12" class="pf w0 h0" data-page-no="12"><div class="pc pc12 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Architecture<span class="_ _a"> </span>Flags<span class="_ _9"> </span>-<span class="_ _a"> </span>32-bits<span class="_ _1d"> </span>o<span class="_ _3"></span>r<span class="_ _1d"> </span>64-bits?<span class="_ _1e"> </span>1/3</div><div class="t m0 x1 hc y89 ff4 fs7 fc0 sc0 ls0 ws0">Architecture-o<span class="_ _3"></span>riented<span class="_ _11"> </span>optimizations<span class="_ _d"> </span>are<span class="_ _d"> </span>not<span class="_ _d"> </span>included<span class="_ _11"> </span>in<span class="_ _d"> </span>other<span class="_ _11"> </span>flags<span class="_ _d"> </span>(<span class="_ _c"> </span><span class="ffd">-O3<span class="_ _c"> </span></span>)</div><div class="t m0 x24 hc y8a ff6 fs7 fc0 sc0 ls0 ws0">-m64<span class="_ _1a"> </span><span class="ff4">In<span class="_ _d"> </span>64-bit<span class="_ _d"> </span>mo<span class="_ _6"></span>de<span class="_ _d"> </span>the<span class="_ _d"> </span>numb<span class="_ _6"></span>er<span class="_ _d"> </span>of<span class="_ _11"> </span>available<span class="_ _11"> </span>registers<span class="_ _d"> </span>increases<span class="_ _11"> </span>from<span class="_ _d"> </span>6<span class="_ _11"> </span>to<span class="_ _d"> </span>14<span class="_ _11"> </span>general</span></div><div class="t m0 x6 hc y8b ff4 fs7 fc0 sc0 ls0 ws0">and<span class="_ _d"> </span>from<span class="_ _11"> </span>8<span class="_ _d"> </span>to<span class="_ _11"> </span>16<span class="_ _d"> </span><span class="ffd">XMM</span>.<span class="_ _11"> </span>Also,<span class="_ _d"> </span>all<span class="_ _11"> </span>64-bits<span class="_ _d"> </span>x86<span class="_ _11"> </span>architectures<span class="_ _d"> </span>have<span class="_ _d"> </span><span class="ffd">SSE2<span class="_ _11"> </span></span>extension<span class="_ _d"> </span>by</div><div class="t m0 x6 hc y8c ff4 fs7 fc0 sc0 ls0 ws0">default.<span class="_ _10"> </span>64-bit<span class="_ _d"> </span>applications<span class="_ _d"> </span>can<span class="_ _11"> </span>use<span class="_ _11"> </span>mo<span class="_ _3"></span>re<span class="_ _11"> </span>than<span class="_ _d"> </span>4GB<span class="_ _11"> </span>address<span class="_ _d"> </span>space</div><div class="t m0 x24 hc y8d ff6 fs7 fc0 sc0 ls0 ws0">-m32<span class="_ _1a"> </span><span class="ff4">32-bit<span class="_ _d"> </span>mode.<span class="_ _10"> </span>It<span class="_ _11"> </span>should<span class="_ _d"> </span>b<span class="_ _6"></span>e<span class="_ _d"> </span>combined<span class="_ _11"> </span>with<span class="_ _17"> </span><span class="ffd">-mfpmath=sse<span class="_ _17"> </span></span>to<span class="_ _d"> </span>enable<span class="_ _11"> </span>using<span class="_ _11"> </span>of<span class="_ _1d"> </span><span class="ffd">XMM</span></span></div><div class="t m0 x6 hc y8e ff4 fs7 fc0 sc0 ls0 ws0">registers<span class="_ _d"> </span>in<span class="_ _11"> </span>floating<span class="_ _d"> </span>p<span class="_ _6"></span>oint<span class="_ _d"> </span>instructions<span class="_ _11"> </span>(instead<span class="_ _d"> </span>of<span class="_ _11"> </span>stack<span class="_ _d"> </span>in<span class="_ _11"> </span>x87<span class="_ _11"> </span>mo<span class="_ _6"></span>de).<span class="_ _a"> </span>32-bit</div><div class="t m0 x6 hc y8f ff4 fs7 fc0 sc0 ls0 ws0">applications<span class="_ _d"> </span>can<span class="_ _11"> </span>use<span class="_ _d"> </span>less<span class="_ _11"> </span>than<span class="_ _d"> </span>4GB<span class="_ _11"> </span>address<span class="_ _d"> </span>space</div><div class="t m0 x1 hc y90 ff4 fs7 fc0 sc0 ls0 ws0">It<span class="_ _d"> </span>is<span class="_ _11"> </span>recommended<span class="_ _d"> </span>to<span class="_ _11"> </span>use<span class="_ _d"> </span>64-bits<span class="_ _11"> </span>for<span class="_ _d"> </span>High-P<span class="_ _3"></span>erformance<span class="_ _d"> </span>Computing<span class="_ _d"> </span>applications<span class="_ _d"> </span>and</div><div class="t m0 x1 hc y91 ff4 fs7 fc0 sc0 ls0 ws0">32-bits<span class="_ _d"> </span>for<span class="_ _d"> </span>phone<span class="_ _d"> </span>and<span class="_ _11"> </span>tablets<span class="_ _d"> </span>applications</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">16/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
|
||
<div id="pf13" class="pf w0 h0" data-page-no="13"><div class="pc pc13 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Architecture<span class="_ _1d"> </span>Flags<span class="_ _1f"> </span>2/3</div><div class="t m0 x5 hc y5c ff6 fs7 fc0 sc0 ls0 ws0">-march=<arch><span class="_ _1a"> </span><span class="ff4">Generates<span class="_ _d"> </span>instructions<span class="_ _d"> </span>for<span class="_ _d"> </span>a<span class="_ _d"> </span>sp<span class="_ _6"></span>ecific<span class="_ _d"> </span>processor<span class="_ _d"> </span>to<span class="_ _d"> </span>exploit<span class="_ _d"> </span>exclusive</span></div><div class="t m0 x25 hc y68 ff4 fs7 fc0 sc0 ls0 ws0">ha<span class="_ _3"></span>rdwa<span class="_ _3"></span>re<span class="_ _d"> </span>features.<span class="_ _4"> </span><span class="ffd"><arch><span class="_ _17"> </span></span>represents<span class="_ _d"> </span>the<span class="_ _d"> </span>minimum<span class="_ _11"> </span>hardw<span class="_ _3"></span>a<span class="_ _3"></span>re</div><div class="t m0 x25 hc y69 ff4 fs7 fc0 sc0 ls0 ws0">supp<span class="_ _6"></span>o<span class="_ _3"></span>rted<span class="_ _d"> </span>by<span class="_ _d"> </span>the<span class="_ _d"> </span>binaries<span class="_ _d"> </span>(not<span class="_ _d"> </span>p<span class="_ _6"></span>o<span class="_ _3"></span>rtable)</div><div class="t m0 x26 hc y92 ff6 fs7 fc0 sc0 ls0 ws0">-mtune=<tune<span class="_ _9"> </span>arch><span class="_ _1a"> </span><span class="ff4">Specifies<span class="_ _11"> </span>the<span class="_ _d"> </span>target<span class="_ _d"> </span>microa<span class="_ _3"></span>rchitecture.<span class="_ _10"> </span>Generates<span class="_ _11"> </span>optimized<span class="_ _d"> </span>co<span class="_ _6"></span>de</span></div><div class="t m0 x25 hc y93 ff4 fs7 fc0 sc0 ls0 ws0">fo<span class="_ _3"></span>r<span class="_ _11"> </span>a<span class="_ _d"> </span>class<span class="_ _11"> </span>of<span class="_ _d"> </span>processors<span class="_ _d"> </span>without<span class="_ _d"> </span>exploiting<span class="_ _11"> </span>sp<span class="_ _6"></span>ecific<span class="_ _d"> </span>hardw<span class="_ _3"></span>a<span class="_ _3"></span>re</div><div class="t m0 x25 hc y94 ff4 fs7 fc0 sc0 ls0 ws0">features.<span class="_ _10"> </span>Bina<span class="_ _3"></span>ries<span class="_ _11"> </span>a<span class="_ _3"></span>re<span class="_ _11"> </span>still<span class="_ _d"> </span>compatibles<span class="_ _11"> </span>with<span class="_ _d"> </span>other<span class="_ _11"> </span>processors,<span class="_ _d"> </span>e.g.</div><div class="t m0 x25 hc y95 ff4 fs7 fc0 sc0 ls0 ws0">ea<span class="_ _3"></span>rlier<span class="_ _11"> </span>CPUs<span class="_ _d"> </span>in<span class="_ _11"> </span>the<span class="_ _d"> </span>architecture<span class="_ _d"> </span>family<span class="_ _d"> </span>(maybe<span class="_ _11"> </span>slow<span class="_ _3"></span>er<span class="_ _d"> </span>than</div><div class="t m0 x27 hc y96 ffd fs7 fc0 sc0 ls0 ws0">-march<span class="_ _c"> </span><span class="ff4">)</span></div><div class="t m0 x20 hc y97 ff6 fs7 fc0 sc0 ls0 ws0">-mcpu=<tune<span class="_ _9"> </span>arch><span class="_ _1a"> </span><span class="ff4">Dep<span class="_ _3"></span>recated<span class="_ _d"> </span>synonym<span class="_ _11"> </span>for<span class="_ _17"> </span><span class="ffd">-mtune<span class="_ _17"> </span></span>fo<span class="_ _3"></span>r<span class="_ _d"> </span><span class="ffd">x86-64<span class="_ _11"> </span></span>processors,<span class="_ _d"> </span>optimizes</span></div><div class="t m0 x25 hc y98 ff4 fs7 fc0 sc0 ls0 ws0">fo<span class="_ _3"></span>r<span class="_ _11"> </span>b<span class="_ _6"></span>oth<span class="_ _d"> </span>a<span class="_ _d"> </span>particula<span class="_ _3"></span>r<span class="_ _d"> </span>architecture<span class="_ _d"> </span>and<span class="_ _d"> </span>microarchitecture<span class="_ _d"> </span>on<span class="_ _d"> </span><span class="ffd">Arm</span></div><div class="t m0 x14 hc y99 ff6 fs7 fc0 sc0 ls0 ws0">-mfpu<fp<span class="_ _9"> </span>hw><span class="_ _1a"> </span><span class="ff4">(Arm)<span class="_ _d"> </span>Optimize<span class="_ _d"> </span>for<span class="_ _d"> </span>a<span class="_ _d"> </span>sp<span class="_ _6"></span>ecific<span class="_ _d"> </span>floating-p<span class="_ _6"></span>oint<span class="_ _d"> </span>hardw<span class="_ _3"></span>a<span class="_ _3"></span>re</span></div><div class="t m0 x10 hc y9a ff6 fs7 fc0 sc0 ls0 ws0">-m<instr<span class="_ _9"> </span>set><span class="_ _1a"> </span><span class="ff4">(x86-64)<span class="_ _d"> </span>Optimize<span class="_ _d"> </span>for<span class="_ _d"> </span>a<span class="_ _d"> </span>sp<span class="_ _6"></span>ecific<span class="_ _d"> </span>instruction<span class="_ _11"> </span>set</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">17/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
|
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<div id="pf14" class="pf w0 h0" data-page-no="14"><div class="pc pc14 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Architecture<span class="_ _1d"> </span>Flags<span class="_ _1f"> </span>3/3</div><div class="t m0 x28 h6 y9b ff6 fs4 fc0 sc0 ls0 ws0"><arch><span class="_ _20"> </span><span class="ffd">armv9-a<span class="_ _c"> </span><span class="ff4">,<span class="_ _1b"> </span></span>armv7-a+neon-vfpv4<span class="_ _c"> </span><span class="ff4">,<span class="_ _1b"> </span></span>znver4<span class="_ _c"> </span><span class="ff4">,<span class="_ _1b"> </span></span>core2<span class="_ _c"> </span><span class="ff4">,<span class="_ _1b"> </span></span>skylake</span></div><div class="t m0 x29 h6 y9c ff6 fs4 fc0 sc0 ls0 ws0"><tune<span class="_ _11"> </span>arch><span class="_ _20"> </span><span class="ffd">cortex-a9<span class="_ _c"> </span><span class="ff4">,<span class="_ _1b"> </span></span>neoverse-n2<span class="_ _c"> </span><span class="ff4">,<span class="_ _1b"> </span></span>generic<span class="_ _c"> </span><span class="ff4">,<span class="_ _1b"> </span></span>intel</span></div><div class="t m0 x29 h6 y9d ff6 fs4 fc0 sc0 ls0 ws0"><instr<span class="_ _11"> </span>set><span class="_ _20"> </span><span class="ffd">see2<span class="_ _c"> </span><span class="ff4">,<span class="_ _1b"> </span></span>avx512</span></div><div class="t m0 x2a h6 y9e ff6 fs4 fc0 sc0 ls0 ws0"><fp<span class="_ _11"> </span>hw><span class="_ _20"> </span><span class="ffd">neon<span class="_ _c"> </span><span class="ff4">,<span class="_ _1b"> </span></span>neon-fp-armv8</span></div><div class="t m0 x10 hc y9f ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _1a"> </span><span class="ff6"><tune<span class="_ _11"> </span>arch><span class="_ _17"> </span><span class="ff4">should<span class="_ _11"> </span>b<span class="_ _6"></span>e<span class="_ _d"> </span>alw<span class="_ _3"></span>ays<span class="_ _d"> </span>greater<span class="_ _d"> </span>than<span class="_ _17"> </span><span class="ff6"><arch></span></span></span></div><div class="t m0 x10 hc ya0 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">In<span class="_ _d"> </span>general,<span class="_ _17"> </span><span class="ff6">-mtune<span class="_ _17"> </span></span>is<span class="_ _11"> </span>set<span class="_ _d"> </span>to<span class="_ _17"> </span><span class="ffd">generic<span class="_ _17"> </span></span>if<span class="_ _11"> </span>not<span class="_ _d"> </span>sp<span class="_ _6"></span>ecified</span></div><div class="t m0 x10 hc ya1 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _1a"> </span><span class="ff6">-march=native<span class="_ _21"> </span><span class="ff4">,<span class="_ _17"> </span></span>-mtune=native<span class="_ _21"> </span><span class="ff4">,<span class="_ _17"> </span></span>-mcpu=native<span class="_ _c"> </span><span class="ff4">:<span class="_ _10"> </span>Allo<span class="_ _3"></span>ws<span class="_ _11"> </span>the<span class="_ _d"> </span>compiler<span class="_ _11"> </span>to</span></span></div><div class="t m0 x6 hc ya2 ff4 fs7 fc0 sc0 ls0 ws0">determine<span class="_ _d"> </span>the<span class="_ _11"> </span>p<span class="_ _3"></span>ro<span class="_ _6"></span>cesso<span class="_ _3"></span>r<span class="_ _11"> </span>type<span class="_ _d"> </span>(not<span class="_ _11"> </span>alwa<span class="_ _3"></span>ys<span class="_ _d"> </span>accurate)</div><div class="t m0 x10 hc ya3 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">Esp<span class="_ _6"></span>ecially<span class="_ _d"> </span>with<span class="_ _d"> </span>new<span class="_ _11"> </span>compilers,<span class="_ _d"> </span>prefer<span class="_ _d"> </span><span class="ff1">auto-vectorization<span class="_ _d"> </span></span>to<span class="_ _d"> </span>explicit<span class="_ _11"> </span>vecto<span class="_ _3"></span>r</span></div><div class="t m0 x6 hc ya4 ff4 fs7 fc0 sc0 ls0 ws0">intrinsics</div><div class="t m0 x12 hd ya5 ffe fs6 fc7 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff9">GCC<span class="_ _a"> </span>Arm<span class="_ _10"> </span>options<span class="fff">,<span class="_ _21"> </span></span>GCC<span class="_ _10"> </span>X86-64<span class="_ _a"> </span>options</span></div><div class="t m0 x12 hd ya6 ffe fs6 fc7 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff9">Compiler<span class="_ _a"> </span>flags<span class="_ _10"> </span>across<span class="_ _a"> </span>architectures:<span class="_ _12"> </span>-march,<span class="_ _a"> </span>-mtune,<span class="_ _10"> </span>and<span class="_ _a"> </span>-mcpu</span></div><div class="t m0 x12 hd ya7 ffe fs6 fc7 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff9">NVIDIA<span class="_ _a"> </span>Grace<span class="_ _10"> </span>CPU<span class="_ _a"> </span>Benchmarking<span class="_ _a"> </span>Guide,<span class="_ _10"> </span>Arm<span class="_ _a"> </span>Vector<span class="_ _a"> </span>Instructions:<span class="_ _12"> </span>SVE<span class="_ _10"> </span>and<span class="_ _a"> </span>NEON</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">18/76</div><a class="l" href="https://gcc.gnu.org/onlinedocs/gcc/ARM-Options.html"><div class="d m1" style="border-style:none;position:absolute;left:49.168000px;bottom:25.386000px;width:72.603000px;height:9.365000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://gcc.gnu.org/onlinedocs/gcc/x86-Options.html"><div class="d m1" style="border-style:none;position:absolute;left:125.410000px;bottom:25.386000px;width:86.725000px;height:9.365000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://community.arm.com/arm-community-blogs/b/tools-software-ides-blog/posts/compiler-flags-across-architectures-march-mtune-and-mcpu"><div class="d m1" style="border-style:none;position:absolute;left:49.168000px;bottom:12.783000px;width:298.555000px;height:9.365000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://nvidia.github.io/grace-cpu-benchmarking-guide/developer/vectorization.html"><div class="d m1" style="border-style:none;position:absolute;left:49.168000px;bottom:0.180000px;width:355.043000px;height:9.366000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf15" class="pf w0 h0" data-page-no="15"><div class="pc pc15 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Help<span class="_ _1d"> </span>the<span class="_ _1d"> </span>Compiler<span class="_ _1d"> </span>to<span class="_ _a"> </span>Pro<span class="_ _6"></span>duce<span class="_ _1d"> </span>Better<span class="_ _1d"> </span>Co<span class="_ _6"></span>de</div><div class="t m0 x10 hc ya8 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">Grouping<span class="_ _d"> </span>va<span class="_ _3"></span>riables<span class="_ _d"> </span>and<span class="_ _d"> </span>functions<span class="_ _c"> </span>related<span class="_ _d"> </span>to<span class="_ _d"> </span>each<span class="_ _d"> </span>other<span class="_ _d"> </span>in<span class="_ _c"> </span>the<span class="_ _d"> </span>same<span class="_ _d"> </span>translation<span class="_ _d"> </span>unit</span></div><div class="t m0 x10 hc ya9 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">Define<span class="_ _d"> </span><span class="ffc">global<span class="_ _11"> </span>variables<span class="_ _9"> </span></span>and<span class="_ _11"> </span><span class="ffc">functions<span class="_ _a"> </span></span>in<span class="_ _d"> </span>the<span class="_ _11"> </span>translation<span class="_ _d"> </span>unit<span class="_ _11"> </span>in<span class="_ _d"> </span>which<span class="_ _11"> </span>they<span class="_ _d"> </span>are</span></div><div class="t m0 x6 hc yaa ff4 fs7 fc0 sc0 ls0 ws0">used<span class="_ _d"> </span>more<span class="_ _d"> </span>often</div><div class="t m0 x10 hc yab ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffc">Global<span class="_ _d"> </span>variables<span class="_ _1d"> </span><span class="ff4">and<span class="_ _d"> </span>functions<span class="_ _11"> </span>that<span class="_ _d"> </span>are<span class="_ _d"> </span>not<span class="_ _d"> </span>used<span class="_ _11"> </span>by<span class="_ _d"> </span>other<span class="_ _d"> </span>translation<span class="_ _d"> </span>units<span class="_ _11"> </span>should</span></span></div><div class="t m0 x6 hc yac ff4 fs7 fc0 sc0 ls0 ws0">have<span class="_ _d"> </span><span class="ffc">internal<span class="_ _11"> </span>linkage<span class="_ _9"> </span></span>(<span class="ffc">anonymous<span class="_ _11"> </span>namespace<span class="_ _6"></span></span>/<span class="_ _c"> </span><span class="ffd">static<span class="_ _17"> </span></span>function)</div><div class="t m0 x1 hc yad ff1 fs7 fc0 sc0 ls0 ws0">Static<span class="_ _11"> </span>libra<span class="_ _3"></span>ry<span class="_ _9"> </span>linking<span class="_ _11"> </span>helps<span class="_ _9"> </span>the<span class="_ _9"> </span>linker<span class="_ _11"> </span>to<span class="_ _9"> </span>optimize<span class="_ _9"> </span>the<span class="_ _11"> </span>co<span class="_ _6"></span>de<span class="_ _9"> </span>across<span class="_ _9"> </span>different</div><div class="t m0 x1 hc yae ff1 fs7 fc0 sc0 ls0 ws0">mo<span class="_ _6"></span>dules<span class="_ _11"> </span>(link-time<span class="_ _9"> </span>optimizations).<span class="_ _10"> </span><span class="ff4">Dynamic<span class="_ _d"> </span>linking<span class="_ _11"> </span>p<span class="_ _3"></span>revents<span class="_ _11"> </span>these<span class="_ _d"> </span>kinds<span class="_ _11"> </span>of</span></div><div class="t m0 x1 hc yaf ff4 fs7 fc0 sc0 ls0 ws0">optimizations</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">19/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf16" class="pf w0 h0" data-page-no="16"><div class="pc pc16 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Profile<span class="_ _1d"> </span>Guided<span class="_ _1d"> </span>Optimization<span class="_ _1d"> </span>(PGO)<span class="_ _22"> </span>1/2</div><div class="t m0 x1 hc yb0 ff1 fs7 fc0 sc0 ls0 ws0">Profile<span class="_ _11"> </span>Guided<span class="_ _9"> </span>Optimization<span class="_ _9"> </span>(PGO)<span class="_ _11"> </span><span class="ff4">is<span class="_ _d"> </span>a<span class="_ _11"> </span>compiler<span class="_ _d"> </span>technique<span class="_ _11"> </span>aims<span class="_ _d"> </span>at<span class="_ _11"> </span>improving<span class="_ _d"> </span>the</span></div><div class="t m0 x1 hc yb1 ff4 fs7 fc0 sc0 ls0 ws0">application<span class="_ _d"> </span>p<span class="_ _6"></span>erfo<span class="_ _3"></span>rmance<span class="_ _11"> </span>b<span class="_ _3"></span>y<span class="_ _11"> </span>reducing<span class="_ _d"> </span>instruction-cache<span class="_ _11"> </span>problems,<span class="_ _d"> </span>reducing<span class="_ _d"> </span>branch</div><div class="t m0 x1 hc yb2 ff4 fs7 fc0 sc0 ls0 ws0">misp<span class="_ _3"></span>redictions,<span class="_ _11"> </span>etc.<span class="_ _10"> </span><span class="ffc">PGO<span class="_ _d"> </span>provides<span class="_ _d"> </span>info<span class="_ _3"></span>rmation<span class="_ _11"> </span>to<span class="_ _d"> </span>the<span class="_ _11"> </span>compiler<span class="_ _d"> </span>ab<span class="_ _6"></span>out<span class="_ _d"> </span>areas<span class="_ _d"> </span>of<span class="_ _d"> </span>an</span></div><div class="t m0 x1 hc yb3 ffc fs7 fc0 sc0 ls0 ws0">application<span class="_ _d"> </span>that<span class="_ _11"> </span>a<span class="_ _3"></span>re<span class="_ _11"> </span>most<span class="_ _d"> </span>frequently<span class="_ _11"> </span>executed</div><div class="t m0 x1 hc yb4 ff4 fs7 fc0 sc0 ls0 ws0">It<span class="_ _d"> </span>consists<span class="_ _11"> </span>in<span class="_ _d"> </span>the<span class="_ _11"> </span>followin<span class="_ _3"></span>g<span class="_ _11"> </span>steps:</div><div class="t m0 x2b hc yb5 ff1 fs7 fc0 sc0 ls0 ws0">(1)<span class="_ _7"> </span><span class="ff4">Compile<span class="_ _d"> </span>and<span class="_ _11"> </span><span class="ffc">instrument<span class="_ _1d"> </span></span>the<span class="_ _11"> </span>co<span class="_ _6"></span>de</span></div><div class="t m0 x2b hc yb6 ff1 fs7 fc0 sc0 ls0 ws0">(2)<span class="_ _7"> </span><span class="ffc">R<span class="_ _3"></span>un<span class="_ _9"> </span><span class="ff4">the<span class="_ _d"> </span>program<span class="_ _d"> </span>b<span class="_ _3"></span>y<span class="_ _11"> </span>exercising<span class="_ _d"> </span>the<span class="_ _11"> </span>most<span class="_ _d"> </span>used/critical<span class="_ _11"> </span>paths</span></span></div><div class="t m0 x2b hc yb7 ff1 fs7 fc0 sc0 ls0 ws0">(3)<span class="_ _7"> </span><span class="ffc">Compile<span class="_ _d"> </span>again<span class="_ _11"> </span><span class="ff4">the<span class="_ _11"> </span>co<span class="_ _6"></span>de<span class="_ _d"> </span>and<span class="_ _11"> </span>exploit<span class="_ _d"> </span>the<span class="_ _11"> </span>info<span class="_ _3"></span>rmation<span class="_ _11"> </span>produced<span class="_ _d"> </span>in<span class="_ _11"> </span>the<span class="_ _d"> </span>previous<span class="_ _d"> </span>step</span></span></div><div class="t m0 x1 hc yb8 ff4 fs7 fc0 sc0 ls0 ws0">The<span class="_ _d"> </span>particula<span class="_ _3"></span>r<span class="_ _d"> </span>options<span class="_ _11"> </span>to<span class="_ _d"> </span>instrument<span class="_ _11"> </span>and<span class="_ _d"> </span>compile<span class="_ _11"> </span>the<span class="_ _d"> </span>co<span class="_ _6"></span>de<span class="_ _d"> </span>are<span class="_ _d"> </span>compiler<span class="_ _d"> </span>sp<span class="_ _6"></span>ecific</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">20/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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||
<div id="pf17" class="pf w0 h0" data-page-no="17"><div class="pc pc17 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Profile<span class="_ _1d"> </span>Guided<span class="_ _1d"> </span>Optimization<span class="_ _1d"> </span>(PGO)<span class="_ _22"> </span>2/2</div><div class="t m0 x1 hc yb9 ff1 fs7 fc0 sc0 ls0 ws0">GCC</div><div class="t m0 x10 ha yba ff10 fs6 fc8 sc0 ls0 ws0">$<span class="_ _a"> </span><span class="ff9 fc0">gcc<span class="_ _a"> </span><span class="fc4">-fprofile-generate<span class="_ _10"> </span></span>my_prog.c<span class="_ _a"> </span>my_prog<span class="_ _10"> </span><span class="fc9">#<span class="_ _a"> </span>program<span class="_ _a"> </span>instrumentation</span></span></div><div class="t m0 x10 ha ybb ff10 fs6 fc8 sc0 ls0 ws0">$<span class="_ _a"> </span><span class="ff9 fc0">.<span class="fc5">/</span>my_prog<span class="_ _a"> </span><span class="fc9">#<span class="_ _10"> </span>run<span class="_ _a"> </span>the<span class="_ _10"> </span>program<span class="_ _a"> </span>(most<span class="_ _a"> </span>critial/common<span class="_ _10"> </span>path)</span></span></div><div class="t m0 x10 ha ybc ff10 fs6 fc8 sc0 ls0 ws0">$<span class="_ _a"> </span><span class="ff9 fc0">gcc<span class="_ _a"> </span><span class="fc4">-fprofile-use<span class="_ _10"> </span><span class="fc5">-</span></span>O3<span class="_ _a"> </span>my_prog.c<span class="_ _10"> </span>my_prog<span class="_ _12"> </span><span class="fc9">#<span class="_ _a"> </span>use<span class="_ _a"> </span>instrumentation<span class="_ _10"> </span>info</span></span></div><div class="t m0 x1 hc ybd ff1 fs7 fc0 sc0 ls0 ws0">Clang</div><div class="t m0 x10 ha ybe ff10 fs6 fc8 sc0 ls0 ws0">$<span class="_ _a"> </span><span class="ff9 fc0">clang<span class="fc5">++<span class="_ _a"> </span><span class="fc4">-fprofile-instr-generate<span class="_ _10"> </span></span></span>my_prog.c<span class="_ _a"> </span>my_prog</span></div><div class="t m0 x10 ha ybf ff10 fs6 fc8 sc0 ls0 ws0">$<span class="_ _a"> </span><span class="ff9 fc0">.<span class="fc5">/</span>my_prog</span></div><div class="t m0 x10 ha yc0 ff10 fs6 fc8 sc0 ls0 ws0">$<span class="_ _a"> </span><span class="ff9 fc0">xcrun<span class="_ _a"> </span>llvm<span class="fc5">-</span>profdata<span class="_ _10"> </span>merge<span class="_ _a"> </span><span class="fc5">-</span>output<span class="_ _10"> </span>default.profdata<span class="_ _a"> </span>default.profraw</span></div><div class="t m0 x10 ha yc1 ff10 fs6 fc8 sc0 ls0 ws0">$<span class="_ _a"> </span><span class="ff9 fc0">clang<span class="fc5">++<span class="_ _a"> </span><span class="fc4">-fprofile-instr-use=</span></span>default.profdata<span class="_ _10"> </span><span class="fc5">-</span>O3<span class="_ _a"> </span>my_prog.c<span class="_ _10"> </span>my_prog</span></div><div class="t m0 x10 hd yc2 fff fs6 fc7 sc0 ls0 ws0"><span class="fca sc0">e.g.</span><span class="_ _9"> </span><span class="fca sc0">Firefo</span><span class="fca sc0">x</span><span class="_ _21"> </span><span class="fca sc0">and</span><span class="_ _c"> </span><span class="fca sc0">Go</span><span class="_ _6"></span><span class="fca sc0">ogle</span><span class="_ _21"> </span><span class="fca sc0">Chrome</span><span class="_ _c"> </span><span class="fca sc0">supp</span><span class="_ _6"></span><span class="fca sc0">o</span><span class="_ _3"></span><span class="fca sc0">rt</span><span class="_ _c"> </span><span class="fca sc0">PGO</span><span class="_ _c"> </span><span class="fca sc0">building</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">21/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
|
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<div id="pf18" class="pf w0 h0" data-page-no="18"><div class="pc pc18 w0 h0"><img class="bi x0 y0 w1 h1" alt="" 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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">PGO,<span class="_ _1d"> </span>L<span class="_ _8"></span>TO<span class="_ _1d"> </span>P<span class="_ _3"></span>erformance</div><div class="t m0 x17 he yc3 ffd fs7 fc0 sc0 ls0 ws0">SPEC<span class="_ _16"> </span>2017<span class="_ _16"> </span>built<span class="_ _16"> </span>with<span class="_ _16"> </span>GCC<span class="_ _16"> </span>10.2<span class="_ _16"> </span>and<span class="_ _16"> </span>-O2</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">22/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">P<span class="_ _3"></span>ost-Pro<span class="_ _6"></span>cessing<span class="_ _1d"> </span>Bina<span class="_ _3"></span>ry<span class="_ _a"> </span>Optimizer<span class="_ _1c"> </span>1/2</div><div class="t m0 x1 hc y48 ff4 fs7 fc0 sc0 ls0 ws0">The<span class="_ _d"> </span>co<span class="_ _6"></span>de<span class="_ _d"> </span>lay<span class="_ _3"></span>out<span class="_ _d"> </span>in<span class="_ _11"> </span>the<span class="_ _d"> </span>final<span class="_ _11"> </span>binary<span class="_ _d"> </span>can<span class="_ _d"> </span>b<span class="_ _6"></span>e<span class="_ _d"> </span>further<span class="_ _d"> </span>optimized<span class="_ _11"> </span>with<span class="_ _d"> </span>a<span class="_ _11"> </span><span class="ff1">p<span class="_ _6"></span>ost-link<span class="_ _11"> </span>binary</span></div><div class="t m0 x1 hc y72 ff1 fs7 fc0 sc0 ls0 ws0">optimizer<span class="_ _11"> </span><span class="ff4">and<span class="_ _d"> </span></span>lay<span class="_ _3"></span>out<span class="_ _11"> </span>optimization<span class="_ _11"> </span><span class="ff4">like<span class="_ _d"> </span><span class="ffd">BOLT<span class="_ _d"> </span></span>or<span class="_ _d"> </span><span class="ffd">Propeller<span class="_ _d"> </span></span>(sampling<span class="_ _d"> </span>or</span></div><div class="t m0 x1 hc yc4 ff4 fs7 fc0 sc0 ls0 ws0">instrumentation<span class="_ _d"> </span>profile)</div><div class="t m0 x10 ha yc5 ff9 fs6 fc7 sc0 ls0 ws0">BOLT:<span class="_ _a"> </span>A<span class="_ _a"> </span>Practical<span class="_ _10"> </span>Binary<span class="_ _a"> </span>Optimizer<span class="_ _10"> </span>for<span class="_ _a"> </span>Data<span class="_ _a"> </span>Centers<span class="_ _10"> </span>and<span class="_ _a"> </span>Beyond</div><div class="t m0 x5 ha yc6 ff9 fs6 fc7 sc0 ls0 ws0">BOLT<span class="_ _a"> </span>optimization<span class="_ _a"> </span>technology<span class="_ _10"> </span>could<span class="_ _a"> </span>bring<span class="_ _10"> </span>obvious<span class="_ _a"> </span>performance<span class="_ _a"> </span>uplift<span class="_ _10"> </span>on<span class="_ _a"> </span>arm<span class="_ _10"> </span>server</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">23/76</div><a class="l" href="https://github.com/facebookincubator/BOLT"><div class="d m1" style="border-style:none;position:absolute;left:215.658000px;bottom:187.869000px;width:24.902000px;height:12.901000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://github.com/google/llvm-propeller"><div class="d m1" style="border-style:none;position:absolute;left:254.719000px;bottom:187.869000px;width:53.537000px;height:12.901000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://research.facebook.com/publications/bolt-a-practical-binary-optimizer-for-data-centers-and-beyond/"><div class="d m1" style="border-style:none;position:absolute;left:34.722000px;bottom:19.145000px;width:293.848000px;height:11.657000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://community.arm.com/arm-community-blogs/b/infrastructure-solutions-blog/posts/bolt-optimization-technology"><div class="d m1" style="border-style:none;position:absolute;left:34.324000px;bottom:4.754000px;width:383.287000px;height:11.154000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">P<span class="_ _3"></span>ost-Pro<span class="_ _6"></span>cessing<span class="_ _1d"> </span>Bina<span class="_ _3"></span>ry<span class="_ _a"> </span>Optimizer<span class="_ _1c"> </span>2/2</div><div class="t m0 x10 ha yc7 ff9 fs6 fc7 sc0 ls0 ws0">The<span class="_ _a"> </span>many<span class="_ _a"> </span>faces<span class="_ _10"> </span>of<span class="_ _a"> </span>LLVM<span class="_ _10"> </span>PGO<span class="_ _a"> </span>and<span class="_ _a"> </span>FDO</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">24/76</div><a class="l" href="https://aaupov.github.io/blog/2023/07/09/pgo"><div class="d m1" style="border-style:none;position:absolute;left:34.722000px;bottom:3.022000px;width:162.043000px;height:13.445000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">P<span class="_ _3"></span>olyhedral<span class="_ _1d"> </span>Optimizations</div><div class="t m0 x1 hc yc8 ff1 fs7 fc0 sc0 ls0 ws0">P<span class="_ _3"></span>olyhedral<span class="_ _9"> </span>optimization<span class="_ _d"> </span><span class="ff4">is<span class="_ _11"> </span>a<span class="_ _11"> </span>compilation<span class="_ _d"> </span>technique<span class="_ _11"> </span>that</span></div><div class="t m0 x1 hc yc9 ff4 fs7 fc0 sc0 ls0 ws0">rely<span class="_ _d"> </span>on<span class="_ _11"> </span>the<span class="_ _d"> </span>representation<span class="_ _d"> </span>of<span class="_ _d"> </span>programs,<span class="_ _d"> </span>esp<span class="_ _6"></span>ecially<span class="_ _d"> </span>those<span class="_ _d"> </span>involving</div><div class="t m0 x1 hc yca ff4 fs7 fc0 sc0 ls0 ws0">nested<span class="_ _d"> </span>lo<span class="_ _6"></span>ops<span class="_ _d"> </span>and<span class="_ _11"> </span>a<span class="_ _3"></span>rrays,<span class="_ _d"> </span>in<span class="_ _d"> </span><span class="ffc">parametric<span class="_ _d"> </span>p<span class="_ _6"></span>olyhedra</span>.<span class="_ _a"> </span>Thanks<span class="_ _11"> </span>to</div><div class="t m0 x1 hc ycb ff4 fs7 fc0 sc0 ls0 ws0">combinato<span class="_ _3"></span>rial<span class="_ _11"> </span>and<span class="_ _d"> </span>geometrical<span class="_ _11"> </span>optimizations<span class="_ _d"> </span>on<span class="_ _11"> </span>these<span class="_ _d"> </span>objects,<span class="_ _11"> </span>the</div><div class="t m0 x1 hc ycc ff4 fs7 fc0 sc0 ls0 ws0">compiler<span class="_ _d"> </span>is<span class="_ _11"> </span>able<span class="_ _d"> </span>to<span class="_ _11"> </span>analyze<span class="_ _d"> </span>and<span class="_ _11"> </span>optimize<span class="_ _d"> </span>the<span class="_ _11"> </span>programs<span class="_ _d"> </span>including<span class="_ _d"> </span><span class="ffc">automatic</span></div><div class="t m0 x1 hc ycd ffc fs7 fc0 sc0 ls0 ws0">pa<span class="_ _3"></span>rallelization<span class="ff4">,<span class="_ _11"> </span></span>data<span class="_ _d"> </span>lo<span class="_ _6"></span>calit<span class="_ _3"></span>y<span class="ff4">,<span class="_ _11"> </span></span>memory<span class="_ _d"> </span>management<span class="ff4">,<span class="_ _d"> </span></span>SIMD<span class="_ _d"> </span>instructions<span class="ff4">,<span class="_ _11"> </span>and<span class="_ _11"> </span></span>co<span class="_ _6"></span>de</div><div class="t m0 x1 hc yce ffc fs7 fc0 sc0 ls0 ws0">generation<span class="_ _d"> </span>for<span class="_ _d"> </span>ha<span class="_ _3"></span>rdwa<span class="_ _3"></span>re<span class="_ _d"> </span>accelerators</div><div class="t m0 x1 hc ycf ff6 fs7 fc0 sc0 ls0 ws0">Polly<span class="_ _21"> </span><span class="ff11 fs8"><span class="_ _11"> </span></span><span class="ff4">is<span class="_ _d"> </span>a<span class="_ _11"> </span>high-level<span class="_ _d"> </span>lo<span class="_ _6"></span>op<span class="_ _d"> </span>and<span class="_ _d"> </span>data-lo<span class="_ _6"></span>cality<span class="_ _c"> </span>optimizer<span class="_ _11"> </span>and<span class="_ _d"> </span>optimization<span class="_ _11"> </span>infrastructure</span></div><div class="t m0 x1 hc yd0 ff4 fs7 fc0 sc0 ls0 ws0">fo<span class="_ _3"></span>r<span class="_ _11"> </span>LL<span class="_ _5"></span>VM</div><div class="t m0 x1 hc yd1 ff6 fs7 fc0 sc0 ls0 ws0">PLUTO<span class="_ _21"> </span><span class="ff11 fs8"><span class="_ _11"> </span></span><span class="ff4">is<span class="_ _11"> </span>an<span class="_ _d"> </span>automatic<span class="_ _11"> </span>pa<span class="_ _3"></span>rallelization<span class="_ _11"> </span>to<span class="_ _6"></span>ol<span class="_ _d"> </span>based<span class="_ _d"> </span>on<span class="_ _11"> </span>the<span class="_ _11"> </span>p<span class="_ _6"></span>olyhedral<span class="_ _d"> </span>mo<span class="_ _6"></span>del</span></div><div class="t m0 x10 hd yd2 fff fs6 fc7 sc0 ls0 ws0">see<span class="_ _c"> </span>also<span class="_ _21"> </span><span class="ff9">Using<span class="_ _10"> </span>Polly<span class="_ _a"> </span>with<span class="_ _10"> </span>Clang</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">25/76</div><a class="l" href="https://polly.llvm.org/"><div class="d m1" style="border-style:none;position:absolute;left:27.350000px;bottom:80.557000px;width:30.629000px;height:12.694000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="http://pluto-compiler.sourceforge.net/"><div class="d m1" style="border-style:none;position:absolute;left:27.350000px;bottom:43.939000px;width:30.629000px;height:12.694000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://polly.llvm.org/docs/UsingPollyWithClang.html"><div class="d m1" style="border-style:none;position:absolute;left:67.359000px;bottom:2.599000px;width:105.553000px;height:13.445000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf1c" class="pf w0 h0" data-page-no="1c"><div class="pc pc1c w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x8 h2 yd3 ff1 fs0 fc0 sc0 ls0 ws0">Compiler</div><div class="t m0 x8 h2 yd4 ff1 fs0 fc0 sc0 ls0 ws0">T<span class="_ _23"></span>ransfo<span class="_ _8"></span>rmation</div><div class="t m0 x8 h2 yd5 ff1 fs0 fc0 sc0 ls0 ws0">T<span class="_ _23"></span>echniques</div><a class="l" href="#pf1c" data-dest-detail='[28,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:105.775000px;bottom:166.803000px;width:241.993000px;height:24.026000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf1c" data-dest-detail='[28,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:105.775000px;bottom:137.252000px;width:241.993000px;height:19.206000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf1c" data-dest-detail='[28,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:105.775000px;bottom:98.061000px;width:127.771000px;height:24.026000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf1d" class="pf w0 h0" data-page-no="1d"><div class="pc pc1d w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Help<span class="_ _1d"> </span>the<span class="_ _1d"> </span>Compiler<span class="_ _1d"> </span>to<span class="_ _a"> </span>Pro<span class="_ _6"></span>duce<span class="_ _1d"> </span>Better<span class="_ _1d"> </span>Co<span class="_ _6"></span>de</div><div class="t m0 x1 hc yd6 ff1 fs7 fc0 sc0 ls0 ws0">Overview<span class="_ _11"> </span>on<span class="_ _9"> </span>compiler<span class="_ _9"> </span>co<span class="_ _6"></span>de<span class="_ _9"> </span>generation<span class="_ _11"> </span>and<span class="_ _9"> </span>transformation:</div><div class="t m0 x10 hc yd7 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffd">Optimizations<span class="_ _16"> </span>in<span class="_ _16"> </span>C++<span class="_ _16"> </span>Compilers</span></div><div class="t m0 x11 hc yd8 ffc fs7 fc0 sc0 ls0 ws0">Matt<span class="_ _d"> </span>Go<span class="_ _6"></span>db<span class="_ _6"></span>olt<span class="ff4">,<span class="_ _d"> </span></span>A<span class="_ _3"></span>CM<span class="_ _11"> </span>Queue</div><div class="t m0 x10 hc yd9 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffd">Compiler<span class="_ _16"> </span>Optimizations</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">26/76</div><a class="l" href="https://dl.acm.org/ft_gateway.cfm?id=3372264&ftid=2096683&dwn=1"><div class="d m1" style="border-style:none;position:absolute;left:49.168000px;bottom:125.404000px;width:173.811000px;height:10.952000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="http://compileroptimizations.com/category/address_optimization.htm"><div class="d m1" style="border-style:none;position:absolute;left:49.168000px;bottom:87.267000px;width:127.993000px;height:10.952000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf1e" class="pf w0 h0" data-page-no="1e"><div class="pc pc1e w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Basic<span class="_ _1d"> </span>Compiler<span class="_ _1d"> </span>T<span class="_ _8"></span>ransfo<span class="_ _3"></span>rmations<span class="_ _24"> </span>1/3</div><div class="t m0 x10 hc y5c ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Constant<span class="_ _9"> </span>folding<span class="ff4">.<span class="_ _a"> </span>Direct<span class="_ _11"> </span>evaluation<span class="_ _11"> </span>constant<span class="_ _d"> </span>expressions<span class="_ _d"> </span>at<span class="_ _d"> </span>compile-time</span></span></div><div class="t m0 x2c ha yda ff6 fs6 fc6 sc0 ls0 ws0">const<span class="_ _a"> </span><span class="fc3">int<span class="_ _a"> </span><span class="ff9 fc0">K<span class="_ _10"> </span><span class="fc5">=<span class="_ _a"> </span>100<span class="_ _10"> </span>*<span class="_ _a"> </span>1234<span class="_ _a"> </span>/<span class="_ _10"> </span>2</span>;</span></span></div><div class="t m0 x10 hc ydb ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Constant<span class="_ _9"> </span>p<span class="_ _3"></span>ropagation<span class="ff4">.<span class="_ _10"> </span>Substituting<span class="_ _d"> </span>the<span class="_ _11"> </span>values<span class="_ _d"> </span>of<span class="_ _11"> </span>known<span class="_ _d"> </span>constants<span class="_ _d"> </span>in</span></span></div><div class="t m0 x6 hc ydc ff4 fs7 fc0 sc0 ls0 ws0">exp<span class="_ _3"></span>ressions<span class="_ _11"> </span>at<span class="_ _d"> </span>compile-time</div><div class="t m0 x2c ha ydd ff6 fs6 fc6 sc0 ls0 ws0">const<span class="_ _a"> </span><span class="fc3">int<span class="_ _a"> </span><span class="ff9 fc0">K<span class="_ _10"> </span><span class="fc5">=<span class="_ _a"> </span>100<span class="_ _10"> </span>*<span class="_ _a"> </span>1234<span class="_ _a"> </span>/<span class="_ _10"> </span>2</span>;</span></span></div><div class="t m0 x2c ha yde ff6 fs6 fc6 sc0 ls0 ws0">const<span class="_ _a"> </span><span class="fc3">int<span class="_ _a"> </span><span class="ff9 fc0">J<span class="_ _10"> </span><span class="fc5">=<span class="_ _a"> </span></span>K<span class="_ _10"> </span><span class="fc5">*<span class="_ _a"> </span>25</span>;</span></span></div><div class="t m0 x10 hc ydf ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Common<span class="_ _9"> </span>sub<span class="_ _6"></span>exp<span class="_ _3"></span>ression<span class="_ _11"> </span>elimination<span class="_ _6"></span><span class="ff4">.<span class="_ _a"> </span>Avoid<span class="_ _c"> </span>computing<span class="_ _11"> </span>identical<span class="_ _11"> </span>and<span class="_ _d"> </span>redundant</span></span></div><div class="t m0 x6 hc ye0 ff4 fs7 fc0 sc0 ls0 ws0">exp<span class="_ _3"></span>ressions</div><div class="t m0 x2c ha ye1 ff6 fs6 fc3 sc0 ls0 ws0">int<span class="_ _a"> </span><span class="ff9 fc0">x<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span></span>y<span class="_ _a"> </span><span class="fc5">*<span class="_ _10"> </span></span>z<span class="_ _a"> </span><span class="fc5">+<span class="_ _a"> </span></span>v;</span></div><div class="t m0 x2c ha ye2 ff6 fs6 fc3 sc0 ls0 ws0">int<span class="_ _a"> </span><span class="ff9 fc0">y<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span></span>y<span class="_ _a"> </span><span class="fc5">*<span class="_ _10"> </span></span>z<span class="_ _a"> </span><span class="fc5">+<span class="_ _a"> </span></span>k;<span class="_ _10"> </span><span class="ff8 fcb">//<span class="_ _a"> </span>y<span class="_ _10"> </span>*<span class="_ _a"> </span>z<span class="_ _a"> </span>is<span class="_ _10"> </span>redundant</span></span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">27/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
|
||
<div id="pf1f" class="pf w0 h0" data-page-no="1f"><div class="pc pc1f w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Compiler<span class="_ _1d"> </span>T<span class="_ _8"></span>ransfo<span class="_ _3"></span>rmations<span class="_ _25"> </span>2/3</div><div class="t m0 x10 hc y5c ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Induction<span class="_ _9"> </span>va<span class="_ _3"></span>riable<span class="_ _9"> </span>elimination<span class="ff4">.<span class="_ _10"> </span>Eliminate<span class="_ _d"> </span>variables<span class="_ _d"> </span>whose<span class="_ _d"> </span>values<span class="_ _11"> </span>a<span class="_ _3"></span>re<span class="_ _11"> </span>dep<span class="_ _6"></span>endent</span></span></div><div class="t m0 x6 hc y68 ff4 fs7 fc0 sc0 ls0 ws0">(induction)</div><div class="t m0 x2c ha ye3 ff6 fs6 fc6 sc0 ls0 ws0">for<span class="_ _a"> </span><span class="ff9 fc0">(</span><span class="fc3">int<span class="_ _a"> </span><span class="ff9 fc0">i<span class="_ _10"> </span><span class="fc5">=<span class="_ _a"> </span>0</span>;<span class="_ _10"> </span>i<span class="_ _a"> </span><span class="fc5"><<span class="_ _a"> </span>10</span>;<span class="_ _10"> </span>i<span class="fc5">++</span>)</span></span></div><div class="t m0 x15 ha ye4 ff9 fs6 fc0 sc0 ls0 ws0">x<span class="_ _a"> </span><span class="fc5">=<span class="_ _a"> </span></span>i<span class="_ _10"> </span><span class="fc5">*<span class="_ _a"> </span>8</span>;</div><div class="t m0 x2c ha ye5 ff8 fs6 fcb sc0 ls0 ws0">//<span class="_ _a"> </span>"x"<span class="_ _a"> </span>can<span class="_ _10"> </span>be<span class="_ _a"> </span>derived<span class="_ _10"> </span>by<span class="_ _a"> </span>knowing<span class="_ _a"> </span>the<span class="_ _10"> </span>value<span class="_ _a"> </span>of<span class="_ _10"> </span>"i"</div><div class="t m0 x10 hc ye6 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Dead<span class="_ _9"> </span>co<span class="_ _6"></span>de<span class="_ _11"> </span>elimination<span class="ff4">.<span class="_ _10"> </span>Elimination<span class="_ _d"> </span>of<span class="_ _11"> </span>co<span class="_ _6"></span>de<span class="_ _d"> </span>which<span class="_ _d"> </span>is<span class="_ _11"> </span>executed<span class="_ _11"> </span>but<span class="_ _d"> </span>whose<span class="_ _11"> </span>result</span></span></div><div class="t m0 x6 hc ye7 ff4 fs7 fc0 sc0 ls0 ws0">is<span class="_ _d"> </span>never<span class="_ _11"> </span>used,<span class="_ _d"> </span>e.g.<span class="_ _10"> </span>dead<span class="_ _11"> </span>sto<span class="_ _3"></span>re</div><div class="t m0 x2c ha ye8 ff6 fs6 fc3 sc0 ls0 ws0">int<span class="_ _a"> </span><span class="ff9 fc0">a<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span></span>b<span class="_ _a"> </span><span class="fc5">*<span class="_ _10"> </span></span>c;</span></div><div class="t m0 x2c ha ye9 ff9 fs6 fc0 sc0 ls0 ws0">...<span class="_ _a"> </span><span class="ff8 fcb">//<span class="_ _a"> </span>"a"<span class="_ _10"> </span>is<span class="_ _a"> </span>never<span class="_ _10"> </span>used,<span class="_ _a"> </span>"b<span class="_ _a"> </span>*<span class="_ _10"> </span>c"<span class="_ _a"> </span>is<span class="_ _10"> </span>not<span class="_ _a"> </span>computed</span></div><div class="t m0 x6 hc yea ffc fs7 fc0 sc0 ls0 ws0">Unreachable<span class="_ _d"> </span>co<span class="_ _6"></span>de<span class="_ _d"> </span>elimination<span class="_ _11"> </span><span class="ff4">instead<span class="_ _11"> </span>involves<span class="_ _d"> </span>removing<span class="_ _11"> </span>co<span class="_ _6"></span>de<span class="_ _d"> </span>that<span class="_ _d"> </span>is<span class="_ _11"> </span>never</span></div><div class="t m0 x6 hc yeb ff4 fs7 fc0 sc0 ls0 ws0">executed</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">28/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
|
||
<div id="pf20" class="pf w0 h0" data-page-no="20"><div class="pc pc20 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA4wAAAH/CAIAAACTgcSzAAAACXBIWXMAABYlAAAWJQFJUiTwAAAIjElEQVR42u3YsU1DQRBF0V30R0Qr14CIXAFCxEjuzE1QDgFF0IEjayMm+WSkOCCYRedUMHrR1fTHp5cGAABlfH6831kBAIBqRCoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAAD96ZloBAIA6IsInFQCAckQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAECkAgCASAUAQKQCAIBIBQBgRZsJbjfnNAIAsKgxxkLX+qQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAAAU0jPTCgAA1BERPqkAAJQjUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAEKkmAABApAIAwC82E9xuzmkEAGBRY4yFrvVJBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAACBSAQAQqQAAIFIBAPhHemZaAQCAOiLCJxUAgHJEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAQKQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAAAgUgEAEKkAACBSAQAQqQAA8Ce26/nBCkBr7fL6ZgQAKjg+n3o/3O/Xr9b2fTcIAAAlfAP3WiAAVA1qtgAAAABJRU5ErkJggg=="/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Compiler<span class="_ _1d"> </span>T<span class="_ _8"></span>ransfo<span class="_ _3"></span>rmations<span class="_ _25"> </span>3/3</div><div class="t m0 x10 hc yec ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Use-define<span class="_ _9"> </span>chain<span class="ff4">.<span class="_ _a"> </span>Avoid<span class="_ _d"> </span>computations<span class="_ _d"> </span>related<span class="_ _11"> </span>to<span class="_ _11"> </span>a<span class="_ _d"> </span>variable<span class="_ _d"> </span>that<span class="_ _d"> </span>happ<span class="_ _6"></span>en<span class="_ _d"> </span>b<span class="_ _6"></span>efo<span class="_ _3"></span>re</span></span></div><div class="t m0 x6 hc yed ff4 fs7 fc0 sc0 ls0 ws0">its<span class="_ _d"> </span>definition</div><div class="t m0 x2c ha yee ff9 fs6 fc0 sc0 ls0 ws0">x<span class="_ _a"> </span><span class="fc5">=<span class="_ _a"> </span></span>i<span class="_ _10"> </span><span class="fc5">*<span class="_ _a"> </span></span>k<span class="_ _10"> </span><span class="fc5">+<span class="_ _a"> </span></span>l;</div><div class="t m0 x2c ha yef ff9 fs6 fc0 sc0 ls0 ws0">x<span class="_ _a"> </span><span class="fc5">=<span class="_ _a"> </span>32</span>;<span class="_ _10"> </span><span class="ff8 fcb">//<span class="_ _a"> </span>"i<span class="_ _10"> </span>*<span class="_ _a"> </span>k<span class="_ _a"> </span>+<span class="_ _10"> </span>l"<span class="_ _a"> </span>is<span class="_ _10"> </span>not<span class="_ _a"> </span>needed</span></div><div class="t m0 x10 hc yf0 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">P<span class="_ _3"></span>eephole<span class="_ _9"> </span>optimization<span class="ff4">.<span class="_ _10"> </span>Replace<span class="_ _d"> </span>a<span class="_ _11"> </span>small<span class="_ _d"> </span>set<span class="_ _11"> </span>of<span class="_ _11"> </span>lo<span class="_ _3"></span>w-level<span class="_ _11"> </span>instructions<span class="_ _d"> </span>with<span class="_ _11"> </span>a</span></span></div><div class="t m0 x6 hc yf1 ff4 fs7 fc0 sc0 ls0 ws0">faster<span class="_ _d"> </span>sequence<span class="_ _11"> </span>of<span class="_ _d"> </span>instructions<span class="_ _11"> </span>with<span class="_ _d"> </span>b<span class="_ _6"></span>etter<span class="_ _d"> </span>p<span class="_ _6"></span>erfo<span class="_ _3"></span>rmance<span class="_ _11"> </span>and<span class="_ _d"> </span>the<span class="_ _11"> </span>same<span class="_ _11"> </span>semantic.</div><div class="t m0 x6 hc yf2 ff4 fs7 fc0 sc0 ls0 ws0">The<span class="_ _d"> </span>optimization<span class="_ _11"> </span>can<span class="_ _d"> </span>involve<span class="_ _11"> </span>pattern<span class="_ _d"> </span>matching</div><div class="t m0 x2c ha yf3 ff9 fs6 fc0 sc0 ls0 ws0">imul<span class="_ _26"> </span>eax<span class="fc5">,<span class="_ _a"> </span></span>eax<span class="fc5">,<span class="_ _a"> </span>8<span class="_ _10"> </span><span class="ff8 fcb">//<span class="_ _a"> </span>a<span class="_ _10"> </span>*<span class="_ _a"> </span>8</span></span></div><div class="t m0 x2c ha yf4 ff9 fs6 fc0 sc0 ls0 ws0">sal<span class="_ _27"> </span>eax<span class="fc5">,<span class="_ _a"> </span>3<span class="_ _28"> </span><span class="ff8 fcb">//<span class="_ _a"> </span>a<span class="_ _a"> </span><<<span class="_ _10"> </span>3<span class="_ _a"> </span>(shift)</span></span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">29/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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||
<div id="pf21" class="pf w0 h0" data-page-no="21"><div class="pc pc21 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Lo<span class="_ _6"></span>op<span class="_ _9"> </span>Unswitching</div><div class="t m0 x10 hc yf5 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Lo<span class="_ _6"></span>op<span class="_ _11"> </span>Unswitching<span class="ff4">.<span class="_ _10"> </span>Split<span class="_ _11"> </span>the<span class="_ _d"> </span>lo<span class="_ _6"></span>op<span class="_ _d"> </span>to<span class="_ _11"> </span>imp<span class="_ _3"></span>rove<span class="_ _11"> </span>data<span class="_ _d"> </span>lo<span class="_ _6"></span>cality<span class="_ _5"></span>,<span class="_ _11"> </span>reduce<span class="_ _d"> </span>lo<span class="_ _6"></span>op</span></span></div><div class="t m0 x6 hc yf6 ff4 fs7 fc0 sc0 ls0 ws0">instructions<span class="_ _d"> </span>(esp<span class="_ _6"></span>ecially<span class="_ _d"> </span>branches),<span class="_ _d"> </span>and<span class="_ _d"> </span>allow<span class="_ _d"> </span>additional<span class="_ _d"> </span>optimizations</div><div class="t m0 x2c ha yf7 ff6 fs6 fc6 sc0 ls0 ws0">for<span class="_ _a"> </span><span class="ff9 fc0">(i<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span>0</span>;<span class="_ _a"> </span>i<span class="_ _10"> </span><span class="fc5"><<span class="_ _a"> </span></span>N;<span class="_ _a"> </span>i<span class="fc5">++</span>)<span class="_ _10"> </span>{</span></div><div class="t m0 x15 ha yf8 ff6 fs6 fc6 sc0 ls0 ws0">if<span class="_ _a"> </span><span class="ff9 fc0">(x)</span></div><div class="t m0 x2d ha yf9 ff9 fs6 fc0 sc0 ls0 ws0">a[i]<span class="_ _a"> </span><span class="fc5">=<span class="_ _a"> </span>0</span>;</div><div class="t m0 x15 h10 yfa ff6 fs6 fc6 sc0 ls0 ws0">else</div><div class="t m0 x2d ha yfb ff9 fs6 fc0 sc0 ls0 ws0">b[i]<span class="_ _a"> </span><span class="fc5">=<span class="_ _a"> </span>0</span>;</div><div class="t m0 x2c ha yfc ff9 fs6 fc0 sc0 ls0 ws0">}</div><div class="t m0 x2c ha yfd ff6 fs6 fc6 sc0 ls0 ws0">if<span class="_ _a"> </span><span class="ff9 fc0">(x)<span class="_ _a"> </span>{</span></div><div class="t m0 x15 ha yfe ff6 fs6 fc6 sc0 ls0 ws0">for<span class="_ _a"> </span><span class="ff9 fc0">(i<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span>0</span>;<span class="_ _a"> </span>i<span class="_ _10"> </span><span class="fc5"><<span class="_ _a"> </span></span>N;<span class="_ _a"> </span>i<span class="fc5">++</span>)</span></div><div class="t m0 x2d ha yff ff9 fs6 fc0 sc0 ls0 ws0">a[i]<span class="_ _a"> </span><span class="fc5">=<span class="_ _a"> </span>0</span>;<span class="_ _10"> </span><span class="ff8 fcb">//<span class="_ _a"> </span>use<span class="_ _10"> </span>memset</span></div><div class="t m0 x2c ha y100 ff9 fs6 fc0 sc0 ls0 ws0">}</div><div class="t m0 x2c ha y101 ff6 fs6 fc6 sc0 ls0 ws0">else<span class="_ _a"> </span><span class="ff9 fc0">{</span></div><div class="t m0 x15 ha y102 ff6 fs6 fc6 sc0 ls0 ws0">for<span class="_ _a"> </span><span class="ff9 fc0">(i<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span>0</span>;<span class="_ _a"> </span>i<span class="_ _10"> </span><span class="fc5"><<span class="_ _a"> </span></span>N;<span class="_ _a"> </span>i<span class="fc5">++</span>)</span></div><div class="t m0 x2d ha y103 ff9 fs6 fc0 sc0 ls0 ws0">b[i]<span class="_ _a"> </span><span class="fc5">=<span class="_ _a"> </span>0</span>;<span class="_ _10"> </span><span class="ff8 fcb">//<span class="_ _a"> </span>use<span class="_ _10"> </span>memset</span></div><div class="t m0 x2c ha y104 ff9 fs6 fc0 sc0 ls0 ws0">}</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">30/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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||
<div id="pf22" class="pf w0 h0" data-page-no="22"><div class="pc pc22 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Lo<span class="_ _6"></span>op<span class="_ _9"> </span>Fusion</div><div class="t m0 x10 hc y105 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Lo<span class="_ _6"></span>op<span class="_ _11"> </span>Fusion<span class="_ _d"> </span><span class="ff4">(jamming).<span class="_ _a"> </span>Merge<span class="_ _11"> </span>multiple<span class="_ _11"> </span>lo<span class="_ _6"></span>ops<span class="_ _d"> </span>to<span class="_ _d"> </span>improve<span class="_ _d"> </span>data<span class="_ _d"> </span>lo<span class="_ _6"></span>calit<span class="_ _3"></span>y<span class="_ _11"> </span>and</span></span></div><div class="t m0 x6 hc y106 ff4 fs7 fc0 sc0 ls0 ws0">p<span class="_ _6"></span>erfo<span class="_ _3"></span>rm<span class="_ _d"> </span>additional<span class="_ _11"> </span>optimizations</div><div class="t m0 x2c ha y107 ff6 fs6 fc6 sc0 ls0 ws0">for<span class="_ _a"> </span><span class="ff9 fc0">(i<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span>0</span>;<span class="_ _a"> </span>i<span class="_ _10"> </span><span class="fc5"><<span class="_ _a"> </span>300</span>;<span class="_ _a"> </span>i<span class="fc5">++</span>)</span></div><div class="t m0 x15 ha y108 ff9 fs6 fc0 sc0 ls0 ws0">a[i]<span class="_ _a"> </span><span class="fc5">=<span class="_ _a"> </span></span>a[i]<span class="_ _10"> </span><span class="fc5">+<span class="_ _a"> </span></span>sqrt(i);</div><div class="t m0 x2c ha y109 ff6 fs6 fc6 sc0 ls0 ws0">for<span class="_ _a"> </span><span class="ff9 fc0">(i<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span>0</span>;<span class="_ _a"> </span>i<span class="_ _10"> </span><span class="fc5"><<span class="_ _a"> </span>300</span>;<span class="_ _a"> </span>i<span class="fc5">++</span>)</span></div><div class="t m0 x15 ha y10a ff9 fs6 fc0 sc0 ls0 ws0">b[i]<span class="_ _a"> </span><span class="fc5">=<span class="_ _a"> </span></span>b[i]<span class="_ _10"> </span><span class="fc5">+<span class="_ _a"> </span></span>sqrt(i);</div><div class="t m0 x2c ha y10b ff6 fs6 fc6 sc0 ls0 ws0">for<span class="_ _a"> </span><span class="ff9 fc0">(i<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span>0</span>;<span class="_ _a"> </span>i<span class="_ _10"> </span><span class="fc5"><<span class="_ _a"> </span>300</span>;<span class="_ _a"> </span>i<span class="fc5">++</span>)<span class="_ _10"> </span>{</span></div><div class="t m0 x15 ha y10c ff9 fs6 fc0 sc0 ls0 ws0">a[i]<span class="_ _a"> </span><span class="fc5">=<span class="_ _a"> </span></span>a[i]<span class="_ _10"> </span><span class="fc5">+<span class="_ _a"> </span></span>sqrt(i);<span class="_ _10"> </span><span class="ff8 fcb">//<span class="_ _a"> </span>sqrt(i)<span class="_ _a"> </span>is<span class="_ _10"> </span>computed<span class="_ _a"> </span>only</span></div><div class="t m0 x15 ha y10d ff9 fs6 fc0 sc0 ls0 ws0">b[i]<span class="_ _a"> </span><span class="fc5">=<span class="_ _a"> </span></span>b[i]<span class="_ _10"> </span><span class="fc5">+<span class="_ _a"> </span></span>sqrt(i);<span class="_ _10"> </span><span class="ff8 fcb">//<span class="_ _a"> </span>one<span class="_ _a"> </span>time</span></div><div class="t m0 x2c ha y10e ff9 fs6 fc0 sc0 ls0 ws0">}</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">31/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
|
||
<div id="pf23" class="pf w0 h0" data-page-no="23"><div class="pc pc23 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Lo<span class="_ _6"></span>op<span class="_ _9"> </span>Fission</div><div class="t m0 x10 hc y10f ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Lo<span class="_ _6"></span>op<span class="_ _11"> </span>Fission<span class="_ _11"> </span><span class="ff4">(distribution).<span class="_ _10"> </span>Split<span class="_ _d"> </span>a<span class="_ _11"> </span>lo<span class="_ _6"></span>op<span class="_ _d"> </span>in<span class="_ _d"> </span>multiple<span class="_ _11"> </span>lo<span class="_ _6"></span>ops<span class="_ _d"> </span>to</span></span></div><div class="t m0 x2c ha y110 ff6 fs6 fc6 sc0 ls0 ws0">for<span class="_ _a"> </span><span class="ff9 fc0">(i<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span>0</span>;<span class="_ _a"> </span>i<span class="_ _10"> </span><span class="fc5"><<span class="_ _a"> </span></span>size;<span class="_ _a"> </span>i<span class="fc5">++</span>)<span class="_ _10"> </span>{</span></div><div class="t m0 x15 ha y111 ff9 fs6 fc0 sc0 ls0 ws0">a[i]<span class="_ _a"> </span><span class="fc5">=<span class="_ _a"> </span></span>b[rand()];<span class="_ _10"> </span><span class="ff8 fcb">//<span class="_ _a"> </span>cache<span class="_ _10"> </span>pollution</span></div><div class="t m0 x15 ha y112 ff9 fs6 fc0 sc0 ls0 ws0">c[i]<span class="_ _a"> </span><span class="fc5">=<span class="_ _a"> </span></span>d[rand()];</div><div class="t m0 x2c ha y113 ff9 fs6 fc0 sc0 ls0 ws0">}</div><div class="t m0 x2c ha y114 ff6 fs6 fc6 sc0 ls0 ws0">for<span class="_ _a"> </span><span class="ff9 fc0">(i<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span>0</span>;<span class="_ _a"> </span>i<span class="_ _10"> </span><span class="fc5"><<span class="_ _a"> </span></span>size;<span class="_ _a"> </span>i<span class="fc5">++</span>)</span></div><div class="t m0 x15 ha y115 ff9 fs6 fc0 sc0 ls0 ws0">a[i]<span class="_ _a"> </span><span class="fc5">=<span class="_ _a"> </span></span>b[rand()];<span class="_ _10"> </span><span class="ff8 fcb">//<span class="_ _a"> </span>better<span class="_ _10"> </span>cache<span class="_ _a"> </span>utilization</span></div><div class="t m0 x2c ha y116 ff6 fs6 fc6 sc0 ls0 ws0">for<span class="_ _a"> </span><span class="ff9 fc0">(i<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span>0</span>;<span class="_ _a"> </span>i<span class="_ _10"> </span><span class="fc5"><<span class="_ _a"> </span></span>size;<span class="_ _a"> </span>i<span class="fc5">++</span>)</span></div><div class="t m0 x15 ha y117 ff9 fs6 fc0 sc0 ls0 ws0">c[i]<span class="_ _a"> </span><span class="fc5">=<span class="_ _a"> </span></span>d[rand()];</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">32/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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||
<div id="pf24" class="pf w0 h0" data-page-no="24"><div class="pc pc24 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Lo<span class="_ _6"></span>op<span class="_ _9"> </span>Interchange</div><div class="t m0 x10 hc y118 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Lo<span class="_ _6"></span>op<span class="_ _11"> </span>Interchange<span class="ff4">.<span class="_ _a"> </span>Exchange<span class="_ _d"> </span>the<span class="_ _d"> </span>order<span class="_ _c"> </span>of<span class="_ _d"> </span>lo<span class="_ _6"></span>op<span class="_ _d"> </span>iterations<span class="_ _d"> </span>to<span class="_ _d"> </span>imp<span class="_ _3"></span>rove<span class="_ _d"> </span>data<span class="_ _d"> </span>lo<span class="_ _6"></span>calit<span class="_ _3"></span>y</span></span></div><div class="t m0 x6 hc y119 ff4 fs7 fc0 sc0 ls0 ws0">and<span class="_ _d"> </span>p<span class="_ _6"></span>erfo<span class="_ _3"></span>rm<span class="_ _11"> </span>additional<span class="_ _d"> </span>optimizations<span class="_ _11"> </span>(e.g.<span class="_ _10"> </span>vecto<span class="_ _3"></span>rization)</div><div class="t m0 x2c ha y11a ff6 fs6 fc6 sc0 ls0 ws0">for<span class="_ _a"> </span><span class="ff9 fc0">(i<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span>0</span>;<span class="_ _a"> </span>i<span class="_ _10"> </span><span class="fc5"><<span class="_ _a"> </span>1000000</span>;<span class="_ _a"> </span>i<span class="fc5">++</span>)<span class="_ _10"> </span>{</span></div><div class="t m0 x15 ha y3 ff6 fs6 fc6 sc0 ls0 ws0">for<span class="_ _a"> </span><span class="ff9 fc0">(j<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span>0</span>;<span class="_ _a"> </span>j<span class="_ _10"> </span><span class="fc5"><<span class="_ _a"> </span>100</span>;<span class="_ _a"> </span>j<span class="fc5">++</span>)</span></div><div class="t m0 x2d ha y11b ff9 fs6 fc0 sc0 ls0 ws0">a[j<span class="_ _a"> </span><span class="fc5">*<span class="_ _a"> </span></span>x<span class="_ _10"> </span><span class="fc5">+<span class="_ _a"> </span></span>i]<span class="_ _10"> </span><span class="fc5">=<span class="_ _a"> </span></span>...;<span class="_ _a"> </span><span class="ff8 fcb">//<span class="_ _10"> </span>low<span class="_ _a"> </span>locality</span></div><div class="t m0 x2c ha y11c ff9 fs6 fc0 sc0 ls0 ws0">}</div><div class="t m0 x2c ha y11d ff6 fs6 fc6 sc0 ls0 ws0">for<span class="_ _a"> </span><span class="ff9 fc0">(j<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span>0</span>;<span class="_ _a"> </span>j<span class="_ _10"> </span><span class="fc5"><<span class="_ _a"> </span>100</span>;<span class="_ _a"> </span>j<span class="fc5">++</span>)<span class="_ _10"> </span>{</span></div><div class="t m0 x15 ha y11e ff6 fs6 fc6 sc0 ls0 ws0">for<span class="_ _a"> </span><span class="ff9 fc0">(i<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span>0</span>;<span class="_ _a"> </span>i<span class="_ _10"> </span><span class="fc5"><<span class="_ _a"> </span>1000000</span>;<span class="_ _a"> </span>i<span class="fc5">++</span>)</span></div><div class="t m0 x2d ha y11f ff9 fs6 fc0 sc0 ls0 ws0">a[j<span class="_ _a"> </span><span class="fc5">*<span class="_ _a"> </span></span>x<span class="_ _10"> </span><span class="fc5">+<span class="_ _a"> </span></span>i]<span class="_ _10"> </span><span class="fc5">=<span class="_ _a"> </span></span>...;<span class="_ _a"> </span><span class="ff8 fcb">//<span class="_ _10"> </span>high<span class="_ _a"> </span>locality</span></div><div class="t m0 x2c ha y120 ff9 fs6 fc0 sc0 ls0 ws0">}</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">33/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
|
||
<div id="pf25" class="pf w0 h0" data-page-no="25"><div class="pc pc25 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Lo<span class="_ _6"></span>op<span class="_ _9"> </span>Tiling</div><div class="t m0 x10 hc y121 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Lo<span class="_ _6"></span>op<span class="_ _11"> </span>Tiling<span class="_ _11"> </span><span class="ff4">(blo<span class="_ _6"></span>cking,<span class="_ _d"> </span>nest<span class="_ _11"> </span>optimization).<span class="_ _10"> </span>P<span class="_ _3"></span>artition<span class="_ _d"> </span>the<span class="_ _d"> </span>iterations<span class="_ _11"> </span>of<span class="_ _d"> </span>multiple</span></span></div><div class="t m0 x6 hc y122 ff4 fs7 fc0 sc0 ls0 ws0">lo<span class="_ _6"></span>ops<span class="_ _d"> </span>to<span class="_ _d"> </span>exploit<span class="_ _11"> </span>data<span class="_ _d"> </span>lo<span class="_ _6"></span>calit<span class="_ _3"></span>y</div><div class="t m0 x2c ha y123 ff6 fs6 fc6 sc0 ls0 ws0">for<span class="_ _a"> </span><span class="ff9 fc0">(i<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span>0</span>;<span class="_ _a"> </span>i<span class="_ _10"> </span><span class="fc5"><<span class="_ _a"> </span></span>N;<span class="_ _a"> </span>i<span class="fc5">++</span>)<span class="_ _10"> </span>{</span></div><div class="t m0 x15 ha y124 ff6 fs6 fc6 sc0 ls0 ws0">for<span class="_ _a"> </span><span class="ff9 fc0">(j<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span>0</span>;<span class="_ _a"> </span>j<span class="_ _10"> </span><span class="fc5"><<span class="_ _a"> </span></span>M;<span class="_ _a"> </span>j<span class="fc5">++</span>)</span></div><div class="t m0 x2d ha y125 ff9 fs6 fc0 sc0 ls0 ws0">a[j<span class="_ _a"> </span><span class="fc5">*<span class="_ _a"> </span></span>N<span class="_ _10"> </span><span class="fc5">+<span class="_ _a"> </span></span>i]<span class="_ _10"> </span><span class="fc5">=<span class="_ _a"> </span></span>...;<span class="_ _a"> </span><span class="ff8 fcb">//<span class="_ _10"> </span>low<span class="_ _a"> </span>locality</span></div><div class="t m0 x2c ha y126 ff9 fs6 fc0 sc0 ls0 ws0">}</div><div class="t m0 x2c ha y127 ff6 fs6 fc6 sc0 ls0 ws0">for<span class="_ _a"> </span><span class="ff9 fc0">(i<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span>0</span>;<span class="_ _a"> </span>i<span class="_ _10"> </span><span class="fc5"><<span class="_ _a"> </span></span>N;<span class="_ _a"> </span>i<span class="_ _10"> </span><span class="fc5">+=<span class="_ _a"> </span></span>TILE_SIZE)<span class="_ _10"> </span>{</span></div><div class="t m0 x15 ha y128 ff6 fs6 fc6 sc0 ls0 ws0">for<span class="_ _a"> </span><span class="ff9 fc0">(j<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span>0</span>;<span class="_ _a"> </span>j<span class="_ _10"> </span><span class="fc5"><<span class="_ _a"> </span></span>M;<span class="_ _a"> </span>j<span class="_ _10"> </span><span class="fc5">+=<span class="_ _a"> </span></span>TILE_SIZE)<span class="_ _10"> </span>{</span></div><div class="t m0 x2d ha y129 ff6 fs6 fc6 sc0 ls0 ws0">for<span class="_ _a"> </span><span class="ff9 fc0">(k<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span>0</span>;<span class="_ _a"> </span>k<span class="_ _10"> </span><span class="fc5"><<span class="_ _a"> </span></span>TILE_SIZE;<span class="_ _a"> </span>k<span class="fc5">++</span>)<span class="_ _10"> </span>{</span></div><div class="t m0 x2e ha y12a ff6 fs6 fc6 sc0 ls0 ws0">for<span class="_ _a"> </span><span class="ff9 fc0">(l<span class="_ _a"> </span><span class="fc5">=<span class="_ _10"> </span>0</span>;<span class="_ _a"> </span>l<span class="_ _10"> </span><span class="fc5"><<span class="_ _a"> </span></span>TILE_SIZE;<span class="_ _a"> </span>l<span class="fc5">++</span>)<span class="_ _10"> </span>{</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">34/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
|
||
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<div id="pf27" class="pf w0 h0" data-page-no="27"><div class="pc pc27 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">External<span class="_ _1d"> </span>Lib<span class="_ _3"></span>raries<span class="_ _29"> </span>1/3</div><div class="t m0 x1 hc y48 ff1 fs7 fc0 sc0 ls0 ws0">Consider<span class="_ _11"> </span>using<span class="_ _9"> </span>optimized<span class="_ _9"> </span><span class="ff7">external<span class="_ _e"> </span></span>libra<span class="_ _3"></span>ries<span class="_ _11"> </span>for<span class="_ _11"> </span>critical<span class="_ _9"> </span>p<span class="_ _3"></span>rogram<span class="_ _9"> </span>op<span class="_ _6"></span>erations</div><div class="t m0 x10 hc y12b ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Comp<span class="_ _3"></span>ressed<span class="_ _9"> </span>Bitmask:<span class="_ _10"> </span><span class="ff4">set<span class="_ _d"> </span>algebraic<span class="_ _d"> </span>op<span class="_ _6"></span>erations</span></span></div><div class="t m0 x2 h6 y12c ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffd">BitMagic<span class="_ _e"> </span>Library</span></div><div class="t m0 x2 h6 y12d ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffd">Roaring<span class="_ _e"> </span>Bitmaps</span></div><div class="t m0 x10 hc y12e ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Ordered<span class="_ _9"> </span>Map/Set:<span class="_ _a"> </span><span class="ffd">B+Tree<span class="_ _11"> </span><span class="ff4">as<span class="_ _11"> </span>replacement<span class="_ _d"> </span>for<span class="_ _d"> </span>red-black<span class="_ _d"> </span>tree</span></span></span></div><div class="t m0 x2 h6 y12f ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffd">STX<span class="_ _e"> </span>B+Tree</span></div><div class="t m0 x2 h6 y130 ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffd">Abseil<span class="_ _e"> </span>B-Tree</span></div><div class="t m0 x10 hc y131 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Hash<span class="_ _9"> </span>T<span class="_ _8"></span>able:<span class="_ _a"> </span><span class="ff4 fs4">(replace<span class="_ _d"> </span>for<span class="_ _1b"> </span><span class="ffd">std::unsorted<span class="_ _d"> </span>set/map<span class="_ _c"> </span></span>)</span></span></div><div class="t m0 x2 h6 y132 ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffd">Google<span class="_ _e"> </span>Sparse/Dense<span class="_ _e"> </span>Hash<span class="_ _e"> </span>Table</span></div><div class="t m0 x2 h6 y133 ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffd">bytell<span class="_ _e"> </span>hashmap</span></div><div class="t m0 x2 h6 y134 ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffd">Facebook<span class="_ _e"> </span>F14<span class="_ _e"> </span>memory<span class="_ _e"> </span>efficient<span class="_ _7"> </span>hash<span class="_ _e"> </span>table</span></div><div class="t m0 x2 h6 y135 ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffd">Abseil<span class="_ _e"> </span>Hashmap<span class="_ _d"> </span><span class="fff fs6">(2x-3x<span class="_ _21"> </span>faster)</span></span></div><div class="t m0 x2 h6 y136 ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffd">Robin<span class="_ _e"> </span>Hood<span class="_ _e"> </span>Hashing</span></div><div class="t m0 x2 h6 y137 ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffd">Comprehensive<span class="_ _e"> </span>C++<span class="_ _e"> </span>Hashmap<span class="_ _e"> </span>Benchmarks<span class="_ _7"> </span>2022</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">35/76</div><a class="l" href="http://bitmagic.io/index.html"><div class="d m1" style="border-style:none;position:absolute;left:70.987000px;bottom:168.681000px;width:85.678000px;height:10.175000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="http://roaringbitmap.org/"><div class="d m1" style="border-style:none;position:absolute;left:70.987000px;bottom:154.933000px;width:80.448000px;height:10.174000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://panthema.net/2007/stx-btree/"><div class="d m1" style="border-style:none;position:absolute;left:70.987000px;bottom:121.839000px;width:54.296000px;height:7.961000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://abseil.io/docs/cpp/guides/container"><div class="d m1" style="border-style:none;position:absolute;left:70.987000px;bottom:108.091000px;width:69.987000px;height:7.960000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://github.com/sparsehash/sparsehash"><div class="d m1" style="border-style:none;position:absolute;left:70.987000px;bottom:70.569000px;width:158.903000px;height:11.125000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://probablydance.com/2018/05/28/a-new-fast-hash-table-in-response-to-googles-new-fast-hash-table/"><div class="d m1" style="border-style:none;position:absolute;left:70.987000px;bottom:56.821000px;width:75.217000px;height:10.175000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://code.fb.com/developer-tools/f14/"><div class="d m1" style="border-style:none;position:absolute;left:70.987000px;bottom:43.073000px;width:211.207000px;height:10.174000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://abseil.io/docs/cpp/guides/container"><div class="d m1" style="border-style:none;position:absolute;left:70.987000px;bottom:29.297000px;width:75.217000px;height:10.959000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://github.com/martinus/robin-hood-hashing"><div class="d m1" style="border-style:none;position:absolute;left:70.987000px;bottom:15.576000px;width:96.139000px;height:10.175000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://martin.ankerl.com/2022/08/27/hashmap-bench-01/"><div class="d m1" style="border-style:none;position:absolute;left:70.987000px;bottom:1.828000px;width:216.437000px;height:10.174000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf28" class="pf w0 h0" data-page-no="28"><div class="pc pc28 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">External<span class="_ _1d"> </span>Lib<span class="_ _3"></span>raries<span class="_ _29"> </span>2/3</div><div class="t m0 x10 hc y5c ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Probabilistic<span class="_ _9"> </span>Set<span class="_ _11"> </span>Query:<span class="_ _10"> </span><span class="ff4">Blo<span class="_ _6"></span>om<span class="_ _d"> </span>filter,<span class="_ _11"> </span>‘<span class="ffd">XOR<span class="_ _16"> </span>filter</span>,<span class="_ _d"> </span><span class="ffd">Facebook’s<span class="_ _16"> </span>Ribbon</span></span></span></div><div class="t m0 x6 hc y68 ffd fs7 fc0 sc0 ls0 ws0">Filter<span class="ff4">,<span class="_ _d"> </span></span>Binary<span class="_ _16"> </span>Fuse<span class="_ _16"> </span>filter</div><div class="t m0 x10 hc y138 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Scan,<span class="_ _9"> </span>p<span class="_ _3"></span>rint,<span class="_ _9"> </span>and<span class="_ _9"> </span>fo<span class="_ _3"></span>rmatting:<span class="_ _10"> </span><span class="ffd">fmt<span class="_ _16"> </span>library<span class="ff4">,<span class="_ _d"> </span></span>scn<span class="_ _16"> </span>library<span class="_ _11"> </span><span class="ff4">instead<span class="_ _d"> </span>of<span class="_ _1d"> </span></span>iostream</span></span></div><div class="t m0 x6 hc y139 ff4 fs7 fc0 sc0 ls0 ws0">o<span class="_ _3"></span>r<span class="_ _11"> </span><span class="ffd">printf/scanf</span></div><div class="t m0 x10 hc y13a ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Random<span class="_ _9"> </span>generato<span class="_ _3"></span>r:<span class="_ _10"> </span><span class="ffd">PCG<span class="_ _d"> </span><span class="ff4">random<span class="_ _11"> </span>generator<span class="_ _d"> </span>instead<span class="_ _d"> </span>of<span class="_ _1d"> </span></span>Mersenne<span class="_ _16"> </span>Twister<span class="_ _11"> </span><span class="ff4">o<span class="_ _3"></span>r</span></span></span></div><div class="t m0 x6 he y13b ffd fs7 fc0 sc0 ls0 ws0">Linear<span class="_ _16"> </span>Congruent</div><div class="t m0 x10 hc y13c ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Non-cryptographic<span class="_ _9"> </span>hash<span class="_ _11"> </span>algorithm:<span class="_ _a"> </span><span class="ffd">xxHash<span class="_ _11"> </span><span class="ff4">instead<span class="_ _d"> </span>of<span class="_ _a"> </span></span>CRC</span></span></div><div class="t m0 x10 hc y13d ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Cryptographic<span class="_ _9"> </span>hash<span class="_ _11"> </span>algorithm:<span class="_ _a"> </span><span class="ffd">BLAKE3<span class="_ _11"> </span><span class="ff4">instead<span class="_ _d"> </span>of<span class="_ _a"> </span></span>MD5<span class="_ _d"> </span><span class="ff4">or<span class="_ _d"> </span></span>SHA</span></span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">36/76</div><a class="l" href="https://arxiv.org/abs/1912.08258"><div class="d m1" style="border-style:none;position:absolute;left:237.132000px;bottom:203.558000px;width:59.265000px;height:11.690000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://engineering.fb.com/2021/07/09/data-infrastructure/ribbon-filter/"><div class="d m1" style="border-style:none;position:absolute;left:301.071000px;bottom:203.558000px;width:125.122000px;height:11.690000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://engineering.fb.com/2021/07/09/data-infrastructure/ribbon-filter/"><div class="d m1" style="border-style:none;position:absolute;left:49.168000px;bottom:187.674000px;width:36.356000px;height:10.952000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://github.com/hexops/fastfilter"><div class="d m1" style="border-style:none;position:absolute;left:90.199000px;bottom:187.674000px;width:105.083000px;height:10.952000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://github.com/fmtlib/fmt"><div class="d m1" style="border-style:none;position:absolute;left:193.243000px;bottom:154.159000px;width:64.992000px;height:11.993000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://github.com/eliaskosunen/scnlib/tree/v1.0"><div class="d m1" style="border-style:none;position:absolute;left:262.894000px;bottom:154.159000px;width:64.993000px;height:11.993000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="http://www.pcg-random.org/"><div class="d m1" style="border-style:none;position:absolute;left:149.638000px;bottom:105.367000px;width:103.386000px;height:11.689000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://cyan4973.github.io/xxHash/"><div class="d m1" style="border-style:none;position:absolute;left:227.253000px;bottom:56.271000px;width:36.356000px;height:11.690000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://github.com/BLAKE3-team/BLAKE3"><div class="d m1" style="border-style:none;position:absolute;left:204.798000px;bottom:22.757000px;width:36.356000px;height:11.689000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf29" class="pf w0 h0" data-page-no="29"><div class="pc pc29 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">External<span class="_ _1d"> </span>Lib<span class="_ _3"></span>raries<span class="_ _29"> </span>3/3</div><div class="t m0 x10 hc y13e ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Linea<span class="_ _3"></span>r<span class="_ _9"> </span>Algebra:<span class="_ _a"> </span><span class="ffd">Eigen<span class="ff4">,<span class="_ _d"> </span></span>Armadillo<span class="ff4">,<span class="_ _11"> </span></span>Blaze</span></span></div><div class="t m0 x10 hc y13f ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">So<span class="_ _3"></span>rt:</span></div><div class="t m0 x2 h6 y140 ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffd">Beating<span class="_ _e"> </span>Up<span class="_ _e"> </span>on<span class="_ _e"> </span>Qsort<span class="ff4">.<span class="_ _a"> </span>Radix-sort<span class="_ _c"> </span>fo<span class="_ _3"></span>r<span class="_ _d"> </span>non-compa<span class="_ _3"></span>rative<span class="_ _d"> </span>elements<span class="_ _c"> </span>(e.g.<span class="_ _4"> </span><span class="ffd">int<span class="_ _21"> </span></span>,</span></span></div><div class="t m0 x2f h6 y141 ffd fs4 fc0 sc0 ls0 ws0">float<span class="_ _21"> </span><span class="ff4">)</span></div><div class="t m0 x2 h6 y142 ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffd">Vectorized<span class="_ _e"> </span>and<span class="_ _e"> </span>performance-portable<span class="_ _e"> </span>Quicksort</span></div><div class="t m0 x10 hc y143 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff6">malloc<span class="_ _9"> </span><span class="ff1">replacement:</span></span></div><div class="t m0 x2 h6 y144 ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffd">tcmalloc<span class="_ _c"> </span><span class="ff4">(Go<span class="_ _6"></span>ogle)</span></span></div><div class="t m0 x2 h6 y145 ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffd">mimalloc<span class="_ _c"> </span><span class="ff4">(Microsoft)</span></span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">37/76</div><a class="l" href="http://eigen.tuxfamily.org"><div class="d m1" style="border-style:none;position:absolute;left:129.561000px;bottom:182.779000px;width:30.629000px;height:11.993000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="arma.sourceforge.net"><div class="d m1" style="border-style:none;position:absolute;left:164.864000px;bottom:182.779000px;width:53.538000px;height:11.993000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://bitbucket.org/blaze-lib/blaze"><div class="d m1" style="border-style:none;position:absolute;left:223.076000px;bottom:182.779000px;width:30.629000px;height:11.993000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://travisdowns.github.io/blog/2019/05/22/sorting.html"><div class="d m1" style="border-style:none;position:absolute;left:70.987000px;bottom:136.945000px;width:101.369000px;height:13.938000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://opensource.googleblog.com/2022/06/Vectorized%20and%20performance%20portable%20Quicksort.html"><div class="d m1" style="border-style:none;position:absolute;left:70.987000px;bottom:110.223000px;width:237.359000px;height:10.175000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="http://goog-perftools.sourceforge.net/doc/tcmalloc.html"><div class="d m1" style="border-style:none;position:absolute;left:70.987000px;bottom:64.676000px;width:43.835000px;height:11.955000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://github.com/microsoft/mimalloc"><div class="d m1" style="border-style:none;position:absolute;left:70.987000px;bottom:50.928000px;width:43.835000px;height:11.955000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf2a" class="pf w0 h0" data-page-no="2a"><div class="pc pc2a w0 h0"><img class="bi x0 y0 w1 h1" alt="" 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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Lib<span class="_ _3"></span>ra<span class="_ _3"></span>ries<span class="_ _a"> </span>and<span class="_ _1d"> </span><span class="ff6">Std<span class="_ _1d"> </span></span>replacements</div><div class="t m0 x10 hc y146 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff6 fcc">Folly</span><span class="ff4">:<span class="_ _10"> </span>P<span class="_ _3"></span>erformance-o<span class="_ _3"></span>riented<span class="_ _d"> </span><span class="ffd">std<span class="_ _11"> </span></span>lib<span class="_ _3"></span>rary<span class="_ _d"> </span>(Facebo<span class="_ _6"></span>ok)</span></div><div class="t m0 x10 hc y147 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff6 fcc">Abseil</span><span class="ff4">:<span class="_ _10"> </span>Op<span class="_ _6"></span>en<span class="_ _d"> </span>source<span class="_ _d"> </span>collection<span class="_ _11"> </span>of<span class="_ _d"> </span>C++<span class="_ _11"> </span>lib<span class="_ _3"></span>raries<span class="_ _d"> </span>dra<span class="_ _3"></span>wn<span class="_ _11"> </span>from<span class="_ _d"> </span>the<span class="_ _11"> </span>most</span></div><div class="t m0 x6 hc y148 ff4 fs7 fc0 sc0 ls0 ws0">fundamental<span class="_ _d"> </span>pieces<span class="_ _11"> </span>of<span class="_ _d"> </span>Go<span class="_ _6"></span>ogle’s<span class="_ _d"> </span>internal<span class="_ _11"> </span>co<span class="_ _6"></span>debase</div><div class="t m0 x10 hc y149 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff6 fcc">Frozen</span><span class="ff4">:<span class="_ _10"> </span>Zero-cost<span class="_ _d"> </span>initialization<span class="_ _11"> </span>fo<span class="_ _3"></span>r<span class="_ _11"> </span>immutable<span class="_ _d"> </span>containers,<span class="_ _11"> </span>fixed-size<span class="_ _d"> </span>containers,</span></div><div class="t m0 x6 hc y14a ff4 fs7 fc0 sc0 ls0 ws0">and<span class="_ _d"> </span>various<span class="_ _d"> </span>algo<span class="_ _3"></span>rithms.</div><div class="t m0 x30 hc y14b ff4 fs7 fc0 sc0 ls0 ws0">A<span class="_ _16"> </span>curated<span class="_ _16"> </span>list<span class="_ _16"> </span>of<span class="_ _16"> </span>a<span class="_ _3"></span>wesome<span class="_ _7"> </span>header-only</div><div class="t m0 x30 hc y14c ff4 fs7 fc0 sc0 ls0 ws0">C++<span class="_ _d"> </span>libra<span class="_ _3"></span>ries</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">38/76</div><a class="l" href="https://github.com/facebook/folly/blob/master/folly/docs/Overview.md"><div class="d m1" style="border-style:none;position:absolute;left:49.168000px;bottom:186.729000px;width:30.629000px;height:12.902000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://abseil.io/"><div class="d m1" style="border-style:none;position:absolute;left:49.168000px;bottom:157.806000px;width:36.356000px;height:11.690000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://github.com/serge-sans-paille/frozen"><div class="d m1" style="border-style:none;position:absolute;left:49.168000px;bottom:113.453000px;width:36.356000px;height:10.932000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://github.com/p-ranav/awesome-hpp"><div class="d m1" style="border-style:none;position:absolute;left:27.350000px;bottom:45.436000px;width:87.032000px;height:22.555000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf2b" class="pf w0 h0" data-page-no="2b"><div class="pc pc2b w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x8 h2 y2b ff1 fs0 fc0 sc0 ls0 ws0">P<span class="_ _8"></span>erfo<span class="_ _3"></span>rmance</div><div class="t m0 x8 h2 y2c ff1 fs0 fc0 sc0 ls0 ws0">Benchma<span class="_ _8"></span>rking</div><a class="l" href="#pf2b" data-dest-detail='[43,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:105.775000px;bottom:154.437000px;width:241.993000px;height:19.206000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="#pf2b" data-dest-detail='[43,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:105.775000px;bottom:115.247000px;width:162.763000px;height:24.025000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf2c" class="pf w0 h0" data-page-no="2c"><div class="pc pc2c w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">P<span class="_ _3"></span>erfo<span class="_ _3"></span>rmance<span class="_ _a"> </span>Benchma<span class="_ _3"></span>rking</div><div class="t m0 x9 h5 y14d ff3 fs3 fc0 sc0 ls0 ws0">P<span class="_ _3"></span>erformance<span class="_ _9"> </span>b<span class="_ _6"></span>enchma<span class="_ _3"></span>rking<span class="_ _1d"> </span>is<span class="_ _1d"> </span>a<span class="_ _9"> </span>non-functional<span class="_ _1d"> </span>test<span class="_ _1d"> </span>fo<span class="_ _6"></span>cused<span class="_ _1d"> </span>on<span class="_ _9"> </span>mea-</div><div class="t m0 x6 h5 y14e ff3 fs3 fc0 sc0 ls0 ws0">suring<span class="_ _11"> </span>the<span class="_ _9"> </span>efficiency<span class="_ _11"> </span>of<span class="_ _9"> </span>a<span class="_ _11"> </span>given<span class="_ _9"> </span>task<span class="_ _11"> </span>or<span class="_ _d"> </span>program<span class="_ _11"> </span>under<span class="_ _11"> </span>a<span class="_ _9"> </span>pa<span class="_ _3"></span>rticular<span class="_ _11"> </span>load</div><div class="t m0 x31 h7 y14f ff1 fs2 fc0 sc0 ls0 ws0">P<span class="_ _3"></span>erfo<span class="_ _3"></span>rmance<span class="_ _e"> </span>b<span class="_ _6"></span>enchma<span class="_ _3"></span>rking<span class="_ _7"> </span>is<span class="_ _e"> </span>ha<span class="_ _3"></span>rd!!</div><div class="t m0 x1 h5 y150 ff3 fs3 fc0 sc0 ls0 ws0">Main<span class="_ _11"> </span>reasons:</div><div class="t m0 x10 h5 y151 ff12 fs3 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff13">What<span class="_ _11"> </span>to<span class="_ _9"> </span>test?</span></div><div class="t m0 x10 h5 y152 ff12 fs3 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff13">W<span class="_ _3"></span>orkload/Dataset<span class="_ _11"> </span>qualit<span class="_ _3"></span>y</span></div><div class="t m0 x10 h5 y153 ff12 fs3 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff13">Cache<span class="_ _11"> </span>b<span class="_ _6"></span>ehavio<span class="_ _3"></span>r</span></div><div class="t m0 x10 h5 y154 ff12 fs3 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff13">Stable<span class="_ _11"> </span>CPU<span class="_ _9"> </span>p<span class="_ _6"></span>erfo<span class="_ _3"></span>rmance</span></div><div class="t m0 x32 h5 y151 ff12 fs3 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff13">Program<span class="_ _11"> </span>memory<span class="_ _11"> </span>la<span class="_ _3"></span>yout</span></div><div class="t m0 x32 h5 y152 ff12 fs3 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff13">Measurement<span class="_ _11"> </span>overhead</span></div><div class="t m0 x32 h5 y153 ff12 fs3 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff13">Compiler<span class="_ _11"> </span>optimizations</span></div><div class="t m0 x32 h5 y154 ff12 fs3 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff13">Metric<span class="_ _11"> </span>evaluation</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">39/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf2d" class="pf w0 h0" data-page-no="2d"><div class="pc pc2d w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">What<span class="_ _1d"> </span>to<span class="_ _1d"> </span>T<span class="_ _8"></span>est?</div><div class="t m0 x10 hc y155 ff4 fs7 fc0 sc0 ls0 ws0">1.<span class="_ _7"> </span><span class="ff1">Identify<span class="_ _9"> </span>p<span class="_ _6"></span>erfo<span class="_ _3"></span>rmance<span class="_ _11"> </span>metrics<span class="_ _6"></span><span class="ff4">:<span class="_ _a"> </span>The<span class="_ _d"> </span>metric(s)<span class="_ _11"> </span>should<span class="_ _11"> </span>b<span class="_ _6"></span>e<span class="_ _d"> </span>strongly<span class="_ _d"> </span>related<span class="_ _11"> </span>to<span class="_ _d"> </span>the</span></span></div><div class="t m0 x6 hc y156 ff4 fs7 fc0 sc0 ls0 ws0">sp<span class="_ _6"></span>ecific<span class="_ _d"> </span>p<span class="_ _3"></span>roblem<span class="_ _11"> </span>and<span class="_ _d"> </span>that<span class="_ _11"> </span>allows<span class="_ _d"> </span>a<span class="_ _d"> </span>comparison<span class="_ _d"> </span>across<span class="_ _d"> </span>different<span class="_ _d"> </span>systems,<span class="_ _11"> </span>e.g.</div><div class="t m0 x6 hc y157 ff4 fs7 fc0 sc0 ls0 ws0">elapsed<span class="_ _d"> </span>time<span class="_ _11"> </span>is<span class="_ _d"> </span>not<span class="_ _11"> </span>a<span class="_ _d"> </span>go<span class="_ _6"></span>o<span class="_ _6"></span>d<span class="_ _d"> </span>metric<span class="_ _d"> </span>in<span class="_ _11"> </span>general<span class="_ _d"> </span>for<span class="_ _d"> </span>measuring<span class="_ _d"> </span>the<span class="_ _11"> </span>throughput</div><div class="t m0 x11 h6 y158 ff4 fs4 fc0 sc0 ls0 ws0">-<span class="_ _7"> </span>Matrix<span class="_ _c"> </span>multiplication:<span class="_ _a"> </span>FLoating-p<span class="_ _6"></span>oint<span class="_ _c"> </span>Op<span class="_ _6"></span>eration<span class="_ _c"> </span>Per<span class="_ _c"> </span>Second<span class="_ _c"> </span>(FLOP/S)</div><div class="t m0 x11 h6 y159 ff4 fs4 fc0 sc0 ls0 ws0">-<span class="_ _7"> </span>Graph<span class="_ _c"> </span>traversing:<span class="_ _a"> </span>Edge<span class="_ _c"> </span>p<span class="_ _6"></span>er<span class="_ _c"> </span>Second<span class="_ _d"> </span>(EPS)</div><div class="t m0 x10 hc y15a ff4 fs7 fc0 sc0 ls0 ws0">2.<span class="_ _7"> </span><span class="ff1">Plan<span class="_ _9"> </span>p<span class="_ _6"></span>erfo<span class="_ _3"></span>rmance<span class="_ _11"> </span>tests<span class="_ _6"></span><span class="ff4">:<span class="_ _a"> </span>Determine<span class="_ _d"> </span>what<span class="_ _11"> </span>part<span class="_ _d"> </span>of<span class="_ _d"> </span>the<span class="_ _d"> </span>problem<span class="_ _d"> </span>is<span class="_ _d"> </span>relevant<span class="_ _11"> </span>for</span></span></div><div class="t m0 x6 hc y15b ff4 fs7 fc0 sc0 ls0 ws0">solving<span class="_ _d"> </span>the<span class="_ _11"> </span>given<span class="_ _d"> </span>problem,<span class="_ _d"> </span>e.g.<span class="_ _10"> </span>excluding<span class="_ _d"> </span>initialization<span class="_ _d"> </span>process</div><div class="t m0 x11 h6 y15c ff4 fs4 fc0 sc0 ls0 ws0">-<span class="_ _7"> </span>Supp<span class="_ _6"></span>ose<span class="_ _c"> </span>a<span class="_ _c"> </span>routine<span class="_ _d"> </span>that<span class="_ _c"> </span>requires<span class="_ _d"> </span>different<span class="_ _c"> </span>steps<span class="_ _d"> </span>and<span class="_ _c"> </span>ask<span class="_ _d"> </span>a<span class="_ _c"> </span>memory<span class="_ _c"> </span>buffer<span class="_ _d"> </span>fo<span class="_ _3"></span>r<span class="_ _d"> </span>each<span class="_ _c"> </span>of</div><div class="t m0 x15 h6 y15d ff4 fs4 fc0 sc0 ls0 ws0">them.<span class="_ _1d"> </span>Memory<span class="_ _c"> </span>allo<span class="_ _6"></span>cations<span class="_ _c"> </span>should<span class="_ _c"> </span>b<span class="_ _6"></span>e<span class="_ _c"> </span>excluded<span class="_ _d"> </span>as<span class="_ _c"> </span>a<span class="_ _d"> </span>user<span class="_ _c"> </span>could<span class="_ _d"> </span>use<span class="_ _c"> </span>a<span class="_ _d"> </span>memory<span class="_ _c"> </span>po<span class="_ _6"></span>ol</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">40/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf2e" class="pf w0 h0" data-page-no="2e"><div class="pc pc2e w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">W<span class="_ _3"></span>o<span class="_ _3"></span>rkload/Dataset<span class="_ _a"> </span>Qualit<span class="_ _3"></span>y</div><div class="t m0 x10 hc y15e ff4 fs7 fc0 sc0 ls0 ws0">1.<span class="_ _7"> </span><span class="ff1">Stress<span class="_ _9"> </span>the<span class="_ _11"> </span>most<span class="_ _9"> </span>imp<span class="_ _6"></span>o<span class="_ _3"></span>rtant<span class="_ _9"> </span>cases<span class="ff4">:<span class="_ _10"> </span>Rare<span class="_ _d"> </span>o<span class="_ _3"></span>r<span class="_ _11"> </span>edge<span class="_ _d"> </span>cases<span class="_ _11"> </span>that<span class="_ _d"> </span>are<span class="_ _d"> </span>not<span class="_ _d"> </span>used<span class="_ _11"> </span>in</span></span></div><div class="t m0 x6 hc y15f ff4 fs7 fc0 sc0 ls0 ws0">real-w<span class="_ _3"></span>orld<span class="_ _d"> </span>applications<span class="_ _d"> </span>or<span class="_ _d"> </span>fa<span class="_ _3"></span>r<span class="_ _11"> </span>from<span class="_ _d"> </span>common<span class="_ _11"> </span>usage<span class="_ _d"> </span>are<span class="_ _d"> </span>less<span class="_ _d"> </span>imp<span class="_ _6"></span>ortant,<span class="_ _d"> </span>e.g.<span class="_ _a"> </span>a<span class="_ _11"> </span>graph</div><div class="t m0 x6 hc y160 ff4 fs7 fc0 sc0 ls0 ws0">p<span class="_ _3"></span>roblem<span class="_ _11"> </span>where<span class="_ _d"> </span>all<span class="_ _11"> </span>vertices<span class="_ _d"> </span>are<span class="_ _d"> </span>not<span class="_ _d"> </span>connected</div><div class="t m0 x10 hc y161 ff4 fs7 fc0 sc0 ls0 ws0">2.<span class="_ _7"> </span><span class="ff1">Use<span class="_ _9"> </span>datasets<span class="_ _11"> </span>that<span class="_ _9"> </span>are<span class="_ _11"> </span>w<span class="_ _3"></span>ell-known<span class="_ _11"> </span>in<span class="_ _9"> </span>the<span class="_ _9"> </span>literature<span class="_ _9"> </span>and<span class="_ _11"> </span>reproducible<span class="ff4">.<span class="_ _10"> </span>Don’t</span></span></div><div class="t m0 x6 hc y162 ff4 fs7 fc0 sc0 ls0 ws0">use<span class="_ _d"> </span>“self-made”<span class="_ _11"> </span>dataset<span class="_ _d"> </span>and,<span class="_ _11"> </span>if<span class="_ _d"> </span>p<span class="_ _6"></span>ossible,<span class="_ _d"> </span>use<span class="_ _11"> </span>public<span class="_ _d"> </span>available<span class="_ _11"> </span>resources</div><div class="t m0 x10 hc y163 ff4 fs7 fc0 sc0 ls0 ws0">3.<span class="_ _7"> </span><span class="ff1">Use<span class="_ _9"> </span>a<span class="_ _11"> </span>reproducible<span class="_ _9"> </span>test<span class="_ _9"> </span>metho<span class="_ _6"></span>dology</span>.<span class="_ _10"> </span>T<span class="_ _8"></span>rying<span class="_ _d"> </span>to<span class="_ _11"> </span>remove<span class="_ _d"> </span>sources<span class="_ _11"> </span>of<span class="_ _d"> </span>“noise”,</div><div class="t m0 x6 hc y164 ff4 fs7 fc0 sc0 ls0 ws0">e.g.<span class="_ _10"> </span>if<span class="_ _d"> </span>the<span class="_ _d"> </span>procedure<span class="_ _11"> </span>is<span class="_ _11"> </span>randomized,<span class="_ _d"> </span>the<span class="_ _11"> </span>test<span class="_ _d"> </span>should<span class="_ _11"> </span>b<span class="_ _6"></span>e<span class="_ _d"> </span>use<span class="_ _d"> </span>with<span class="_ _11"> </span>the<span class="_ _d"> </span>same<span class="_ _11"> </span>seed.<span class="_ _10"> </span>It</div><div class="t m0 x6 hc y165 ff4 fs7 fc0 sc0 ls0 ws0">is<span class="_ _d"> </span>not<span class="_ _d"> </span>alwa<span class="_ _3"></span>ys<span class="_ _d"> </span>p<span class="_ _6"></span>ossible,<span class="_ _d"> </span>e.g.<span class="_ _10"> </span>OS<span class="_ _11"> </span>scheduler,<span class="_ _d"> </span>atomic<span class="_ _11"> </span>op<span class="_ _6"></span>erations<span class="_ _d"> </span>in<span class="_ _d"> </span>parallel<span class="_ _d"> </span>computing,</div><div class="t m0 x6 hc y166 ff4 fs7 fc0 sc0 ls0 ws0">etc.</div><div class="t m0 x10 hd y167 fff fs6 fc7 sc0 ls0 ws0">see<span class="_ _c"> </span>also<span class="_ _21"> </span><span class="ff9">Reproducibility<span class="_ _10"> </span>in<span class="_ _a"> </span>artificial<span class="_ _10"> </span>intelligence</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">41/76</div><a class="l" href="https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/17248/15864"><div class="d m1" style="border-style:none;position:absolute;left:67.359000px;bottom:1.331000px;width:199.700000px;height:13.445000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf2f" class="pf w0 h0" data-page-no="2f"><div class="pc pc2f w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Cache<span class="_ _1d"> </span>Behavio<span class="_ _3"></span>r<span class="_ _2a"> </span>1/2</div><div class="t m0 x10 hc y168 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffc">Cache<span class="_ _d"> </span>b<span class="_ _6"></span>ehavio<span class="_ _3"></span>r<span class="_ _d"> </span>is<span class="_ _d"> </span>not<span class="_ _d"> </span>deterministic<span class="ff4">.<span class="_ _10"> </span>Different<span class="_ _d"> </span>executions<span class="_ _d"> </span>lead<span class="_ _d"> </span>to<span class="_ _d"> </span>different<span class="_ _11"> </span>hit<span class="_ _d"> </span>rates</span></span></div><div class="t m0 x10 hc y169 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">After<span class="_ _d"> </span>a<span class="_ _11"> </span>data<span class="_ _d"> </span>is<span class="_ _11"> </span>loaded<span class="_ _d"> </span>from<span class="_ _11"> </span>the<span class="_ _11"> </span>main<span class="_ _d"> </span>memory<span class="_ _8"></span>,<span class="_ _d"> </span>it<span class="_ _d"> </span>remains<span class="_ _11"> </span>in<span class="_ _d"> </span>the<span class="_ _11"> </span>cache<span class="_ _d"> </span>until<span class="_ _11"> </span>it</span></div><div class="t m0 x6 hc y16a ff4 fs7 fc0 sc0 ls0 ws0">expires<span class="_ _d"> </span>or<span class="_ _d"> </span>is<span class="_ _d"> </span>evicted<span class="_ _11"> </span>to<span class="_ _d"> </span>make<span class="_ _d"> </span>ro<span class="_ _6"></span>om<span class="_ _d"> </span>fo<span class="_ _3"></span>r<span class="_ _11"> </span>new<span class="_ _d"> </span>content</div><div class="t m0 x10 hc y16b ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">Executing<span class="_ _d"> </span>the<span class="_ _11"> </span>same<span class="_ _d"> </span>routine<span class="_ _11"> </span>multiple<span class="_ _d"> </span>times,<span class="_ _11"> </span>the<span class="_ _11"> </span>first<span class="_ _d"> </span>run<span class="_ _11"> </span>is<span class="_ _d"> </span>much<span class="_ _11"> </span>slo<span class="_ _3"></span>wer<span class="_ _d"> </span>than<span class="_ _d"> </span>the</span></div><div class="t m0 x6 hc y16c ff4 fs7 fc0 sc0 ls0 ws0">other<span class="_ _d"> </span>ones<span class="_ _11"> </span>due<span class="_ _d"> </span>to<span class="_ _11"> </span>the<span class="_ _d"> </span>cache<span class="_ _11"> </span>effect<span class="_ _d"> </span>(wa<span class="_ _3"></span>rmup<span class="_ _d"> </span>run)</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">42/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf30" class="pf w0 h0" data-page-no="30"><div class="pc pc30 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Cache<span class="_ _1d"> </span>Behavio<span class="_ _3"></span>r<span class="_ _2a"> </span>2/2</div><div class="t m0 x1 hc y16d ffc fs7 fc0 sc0 ls0 ws0">There<span class="_ _d"> </span>is<span class="_ _11"> </span>no<span class="_ _d"> </span>a<span class="_ _11"> </span>systematic<span class="_ _d"> </span>wa<span class="_ _3"></span>y<span class="_ _d"> </span>to<span class="_ _11"> </span>flush<span class="_ _d"> </span>the<span class="_ _11"> </span>cache<span class="ff4">.<span class="_ _10"> </span>Some<span class="_ _d"> </span>techniques<span class="_ _11"> </span>to<span class="_ _d"> </span>ensure<span class="_ _11"> </span>more</span></div><div class="t m0 x1 hc y16e ff4 fs7 fc0 sc0 ls0 ws0">reliable<span class="_ _d"> </span>p<span class="_ _6"></span>erfo<span class="_ _3"></span>rmance<span class="_ _11"> </span>results<span class="_ _d"> </span>are</div><div class="t m0 x10 hc y16f ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">overwrite<span class="_ _d"> </span>all<span class="_ _11"> </span>data<span class="_ _d"> </span>involved<span class="_ _11"> </span>in<span class="_ _d"> </span>the<span class="_ _11"> </span>computation<span class="_ _11"> </span>b<span class="_ _6"></span>et<span class="_ _3"></span>w<span class="_ _3"></span>een<span class="_ _11"> </span>each<span class="_ _d"> </span>runs</span></div><div class="t m0 x10 hc y170 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">read/write<span class="_ _d"> </span>b<span class="_ _6"></span>et<span class="_ _3"></span>ween<span class="_ _d"> </span>t<span class="_ _3"></span>wo<span class="_ _d"> </span>buffers<span class="_ _d"> </span>of<span class="_ _11"> </span>size<span class="_ _d"> </span>at<span class="_ _11"> </span>least<span class="_ _11"> </span>the<span class="_ _d"> </span>size<span class="_ _11"> </span>of<span class="_ _d"> </span>the<span class="_ _11"> </span>la<span class="_ _3"></span>rgest<span class="_ _11"> </span>cache</span></div><div class="t m0 x10 hc y171 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">some<span class="_ _d"> </span>processors,<span class="_ _d"> </span>such<span class="_ _d"> </span>as<span class="_ _11"> </span>ARM,<span class="_ _d"> </span>provide<span class="_ _d"> </span>sp<span class="_ _6"></span>ecific<span class="_ _d"> </span>instructions<span class="_ _d"> </span>to<span class="_ _11"> </span><span class="ffc">invalidate<span class="_ _9"> </span></span>the</span></div><div class="t m0 x6 hc y172 ff4 fs7 fc0 sc0 ls0 ws0">cache<span class="_ _2b"> </span><span class="ffd">builtin<span class="_ _2c"> </span>clear<span class="_ _9"> </span>cache()<span class="_ _c"> </span></span>,<span class="_ _2b"> </span><span class="ffd">clear<span class="_ _9"> </span>cache()</span></div><div class="t m0 x1 h6 y173 ffc fs4 fc0 sc0 ls0 ws0">Note:<span class="_ _a"> </span><span class="ff4">manual<span class="_ _d"> </span>cache<span class="_ _c"> </span>invalidation<span class="_ _d"> </span>must<span class="_ _c"> </span>consider<span class="_ _d"> </span>cache<span class="_ _c"> </span>lo<span class="_ _6"></span>calit<span class="_ _3"></span>y<span class="_ _d"> </span>(e.g.<span class="_ _1d"> </span>L1<span class="_ _c"> </span>p<span class="_ _6"></span>er<span class="_ _c"> </span>CPU<span class="_ _d"> </span>core)<span class="_ _c"> </span>and</span></div><div class="t m0 x1 h6 y174 ff4 fs4 fc0 sc0 ls0 ws0">compiler<span class="_ _c"> </span>optimizations<span class="_ _d"> </span>that<span class="_ _c"> </span>can<span class="_ _d"> </span>remove<span class="_ _c"> </span>useless<span class="_ _d"> </span>co<span class="_ _6"></span>de<span class="_ _c"> </span>(solution:<span class="_ _1d"> </span>use<span class="_ _d"> </span>global<span class="_ _c"> </span>variables<span class="_ _c"> </span>and</div><div class="t m0 xb h6 y175 ffd fs4 fc0 sc0 ls0 ws0">volatile<span class="_ _21"> </span><span class="ff4">)</span></div><div class="t m0 x10 hd y176 fff fs6 fc7 sc0 ls0 ws0">see:<span class="_ _9"> </span><span class="ff9">Is<span class="_ _a"> </span>there<span class="_ _10"> </span>a<span class="_ _a"> </span>way<span class="_ _a"> </span>to<span class="_ _10"> </span>flush<span class="_ _a"> </span>the<span class="_ _10"> </span>entire<span class="_ _a"> </span>CPU<span class="_ _a"> </span>cache<span class="_ _10"> </span>related<span class="_ _a"> </span>to<span class="_ _a"> </span>a<span class="_ _10"> </span>program?</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">43/76</div><a class="l" href="https://stackoverflow.com/questions/48527189/is-there-a-way-to-flush-the-entire-cpu-cache-related-to-a-program"><div class="d m1" style="border-style:none;position:absolute;left:53.102000px;bottom:0.903000px;width:312.677000px;height:13.445000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf31" class="pf w0 h0" data-page-no="31"><div class="pc pc31 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Stable<span class="_ _1d"> </span>CPU<span class="_ _1d"> </span>Perfo<span class="_ _3"></span>rmance<span class="_ _2d"> </span>1/4</div><div class="t m0 x1 hc y48 ff4 fs7 fc0 sc0 ls0 ws0">One<span class="_ _d"> </span>of<span class="_ _11"> </span>the<span class="_ _d"> </span>first<span class="_ _11"> </span>source<span class="_ _d"> </span>of<span class="_ _11"> </span>fluctuation<span class="_ _d"> </span>in<span class="_ _11"> </span>p<span class="_ _6"></span>erfo<span class="_ _3"></span>rmance<span class="_ _11"> </span>measurement<span class="_ _d"> </span>is<span class="_ _11"> </span>due<span class="_ _d"> </span>to<span class="_ _11"> </span>unstable</div><div class="t m0 x1 hc y72 ff4 fs7 fc0 sc0 ls0 ws0">CPU<span class="_ _d"> </span>frequency</div><div class="t m0 x1 hc y177 ff1 fs7 fc0 sc0 ls0 ws0">Dynamic<span class="_ _11"> </span>frequency<span class="_ _9"> </span>scaling<span class="ff4">,<span class="_ _11"> </span>also<span class="_ _d"> </span>known<span class="_ _d"> </span>as<span class="_ _d"> </span><span class="ffc">CPU<span class="_ _11"> </span>throttling</span>,<span class="_ _d"> </span>automatically<span class="_ _11"> </span>decreases</span></div><div class="t m0 x1 hc y178 ff4 fs7 fc0 sc0 ls0 ws0">the<span class="_ _d"> </span>CPU<span class="_ _11"> </span>frequency<span class="_ _d"> </span>for:</div><div class="t m0 x12 h6 y179 ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">Po<span class="_ _3"></span>w<span class="_ _3"></span>er<span class="_ _d"> </span>saving,<span class="_ _c"> </span>extending<span class="_ _d"> </span>battery<span class="_ _c"> </span>life</span></div><div class="t m0 x12 h6 y17a ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">Decrease<span class="_ _c"> </span>fan<span class="_ _d"> </span>noise<span class="_ _c"> </span>and<span class="_ _d"> </span>chip<span class="_ _c"> </span>heat</span></div><div class="t m0 x12 h6 y17b ffb fs4 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">Prevent<span class="_ _c"> </span>high<span class="_ _d"> </span>frequency<span class="_ _c"> </span>damage</span></div><div class="t m0 x1 hc y17c ff4 fs7 fc0 sc0 ls0 ws0">Mo<span class="_ _6"></span>dern<span class="_ _d"> </span>p<span class="_ _3"></span>ro<span class="_ _6"></span>cesso<span class="_ _3"></span>rs<span class="_ _11"> </span>also<span class="_ _d"> </span>comprise<span class="_ _d"> </span>advanced<span class="_ _d"> </span>technologies<span class="_ _11"> </span>to<span class="_ _d"> </span>automatically<span class="_ _11"> </span><span class="ff1">raise<span class="_ _9"> </span>CPU</span></div><div class="t m0 x1 hc y17d ff1 fs7 fc0 sc0 ls0 ws0">op<span class="_ _6"></span>erating<span class="_ _11"> </span>frequency<span class="_ _9"> </span>when<span class="_ _9"> </span>demanding<span class="_ _9"> </span>tasks<span class="_ _11"> </span>are<span class="_ _11"> </span>running<span class="_ _11"> </span><span class="ff4">(e.g.<span class="_ _10"> </span>Intel<span class="ffb">®<span class="_ _d"> </span></span>T<span class="_ _3"></span>urbo</span></div><div class="t m0 x1 hc y17e ff4 fs7 fc0 sc0 ls0 ws0">Bo<span class="_ _6"></span>ost).<span class="_ _a"> </span>Such<span class="_ _11"> </span>technologies<span class="_ _d"> </span>allow<span class="_ _d"> </span>p<span class="_ _3"></span>ro<span class="_ _6"></span>cesso<span class="_ _3"></span>rs<span class="_ _11"> </span>to<span class="_ _d"> </span>run<span class="_ _11"> </span>with<span class="_ _d"> </span>the<span class="_ _11"> </span><span class="ffc">highest<span class="_ _d"> </span>p<span class="_ _6"></span>ossible<span class="_ _d"> </span>frequency</span></div><div class="t m0 x1 hc y17f ff4 fs7 fc0 sc0 ls0 ws0">fo<span class="_ _3"></span>r<span class="_ _11"> </span>limited<span class="_ _d"> </span>amount<span class="_ _11"> </span>of<span class="_ _d"> </span>time<span class="_ _11"> </span>dep<span class="_ _6"></span>ending<span class="_ _d"> </span>on<span class="_ _d"> </span>different<span class="_ _11"> </span>factors<span class="_ _d"> </span>lik<span class="_ _3"></span>e<span class="_ _11"> </span><span class="ffc">t<span class="_ _3"></span>yp<span class="_ _6"></span>e<span class="_ _d"> </span>of<span class="_ _11"> </span>wo<span class="_ _3"></span>rkload<span class="ff4">,</span></span></div><div class="t m0 x1 hc y180 ffc fs7 fc0 sc0 ls0 ws0">numb<span class="_ _6"></span>er<span class="_ _d"> </span>of<span class="_ _d"> </span>active<span class="_ _11"> </span>co<span class="_ _3"></span>res<span class="ff4">,<span class="_ _11"> </span></span>p<span class="_ _6"></span>o<span class="_ _3"></span>wer<span class="_ _d"> </span>consumption<span class="ff4">,<span class="_ _d"> </span></span>temp<span class="_ _6"></span>erature<span class="ff4">,<span class="_ _d"> </span>etc.</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">44/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf32" class="pf w0 h0" data-page-no="32"><div class="pc pc32 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Stable<span class="_ _1d"> </span>CPU<span class="_ _1d"> </span>Perfo<span class="_ _3"></span>rmance<span class="_ _2d"> </span>2/4</div><div class="t m0 x1 hc y181 ff4 fs7 fc0 sc0 ls0 ws0">Get<span class="_ _d"> </span>CPU<span class="_ _11"> </span>info:</div><div class="t m0 x10 hc y182 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffc">CPU<span class="_ _d"> </span>characteristics<span class="_ _0"></span><span class="ff4">:</span></span></div><div class="t m0 x2c hf y183 ff6 fs7 fc0 sc0 ls0 ws0">lscpu</div><div class="t m0 x10 hc y184 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffc">Monito<span class="_ _3"></span>r<span class="_ _11"> </span>CPU<span class="_ _d"> </span>clo<span class="_ _6"></span>cks<span class="_ _d"> </span>in<span class="_ _11"> </span>real-time<span class="_ _6"></span><span class="ff4">:</span></span></div><div class="t m0 x2c hf y185 ff6 fs7 fc0 sc0 ls0 ws0">cpupower<span class="_ _16"> </span>monitor<span class="_ _16"> </span>-m<span class="_ _16"> </span>Mperf</div><div class="t m0 x10 hc y186 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffc">Get<span class="_ _d"> </span>CPU<span class="_ _11"> </span>clo<span class="_ _6"></span>cks<span class="_ _d"> </span>info<span class="_ _6"></span><span class="ff4">:</span></span></div><div class="t m0 x2c hf y187 ff6 fs7 fc0 sc0 ls0 ws0">cpupower<span class="_ _16"> </span>frequency-info</div><div class="t m0 x6 hc y188 ff4 fs7 fc0 sc0 ls0 ws0">see<span class="_ _d"> </span>“<span class="ffd">cpufreq<span class="_ _16"> </span>governors</span>”</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">45/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf33" class="pf w0 h0" data-page-no="33"><div class="pc pc33 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Stable<span class="_ _1d"> </span>CPU<span class="_ _1d"> </span>Perfo<span class="_ _3"></span>rmance<span class="_ _2d"> </span>3/4</div><div class="t m0 x10 hc y5c ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffc">Disable<span class="_ _d"> </span>T<span class="_ _8"></span>urb<span class="_ _6"></span>o<span class="_ _d"> </span>Bo<span class="_ _6"></span>ost</span></div><div class="t m0 x2c h9 y68 ff6 fs4 fc0 sc0 ls0 ws0">echo<span class="_ _e"> </span>1<span class="_ _e"> </span>>><span class="_ _e"> </span>/sys/devices/system/cpu/intel<span class="_ _11"> </span>pstate/no<span class="_ _11"> </span>turbo</div><div class="t m0 x10 hc y189 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffc">Disable<span class="_ _d"> </span>hyp<span class="_ _6"></span>er<span class="_ _d"> </span>threading</span></div><div class="t m0 x2c h9 y18a ff6 fs4 fc0 sc0 ls0 ws0">echo<span class="_ _e"> </span>0<span class="_ _e"> </span>><span class="_ _e"> </span>/sys/devices/system/cpu/cpuX/online</div><div class="t m0 x6 hc y18b ff4 fs7 fc0 sc0 ls0 ws0">o<span class="_ _3"></span>r<span class="_ _11"> </span>through<span class="_ _d"> </span>BIOS</div><div class="t m0 x10 hc y18c ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffc">Use<span class="_ _9"> </span><span class="ff4">“<span class="ffd">performance</span>”<span class="_ _11"> </span></span>scaling<span class="_ _d"> </span>governor</span></div><div class="t m0 x2c hf y18d ff6 fs7 fc0 sc0 ls0 ws0">sudo<span class="_ _16"> </span>cpupower<span class="_ _16"> </span>frequency-set<span class="_ _16"> </span>-g<span class="_ _16"> </span>performance</div><div class="t m0 x10 hc y18e ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffc">Set<span class="_ _d"> </span>CPU<span class="_ _11"> </span>affinity<span class="_ _1d"> </span><span class="ff4">(CPU-Program<span class="_ _11"> </span>binding)<span class="_ _17"> </span><span class="ff6 fs4">taskset<span class="_ _e"> </span>-c<span class="_ _e"> </span><cpu<span class="_ _11"> </span>id><span class="_ _7"> </span><program></span></span></span></div><div class="t m0 x10 hc y18f ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffc">Set<span class="_ _d"> </span>process<span class="_ _11"> </span>prio<span class="_ _3"></span>rit<span class="_ _3"></span>y<span class="_ _4"> </span><span class="ff6 fs4">sudo<span class="_ _e"> </span>nice<span class="_ _e"> </span>-n<span class="_ _7"> </span>-5<span class="_ _e"> </span>taskset<span class="_ _e"> </span>-c<span class="_ _e"> </span><cpu<span class="_ _11"> </span>id><span class="_ _7"> </span><process></span></span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">46/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf34" class="pf w0 h0" data-page-no="34"><div class="pc pc34 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Stable<span class="_ _1d"> </span>CPU<span class="_ _1d"> </span>Perfo<span class="_ _3"></span>rmance<span class="_ _2d"> </span>4/4</div><div class="t m0 x10 hc y190 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffc">Disable<span class="_ _d"> </span>address<span class="_ _11"> </span>space<span class="_ _d"> </span>randomization</span></div><div class="t m0 x2c h9 y191 ff6 fs4 fc0 sc0 ls0 ws0">echo<span class="_ _e"> </span>0<span class="_ _e"> </span>|<span class="_ _e"> </span>sudo<span class="_ _e"> </span>tee<span class="_ _7"> </span>/proc/sys/kernel/randomize<span class="_ _11"> </span>va<span class="_ _11"> </span>space</div><div class="t m0 x10 hc y192 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffc">Drop<span class="_ _d"> </span>file<span class="_ _11"> </span>system<span class="_ _d"> </span>cache<span class="_ _1d"> </span><span class="ff4">(if<span class="_ _11"> </span>the<span class="_ _d"> </span>b<span class="_ _6"></span>enchma<span class="_ _3"></span>rk<span class="_ _11"> </span>involves<span class="_ _d"> </span>IO<span class="_ _11"> </span>ops)</span></span></div><div class="t m0 x2c h9 y193 ff6 fs4 fc0 sc0 ls0 ws0">echo<span class="_ _e"> </span>3<span class="_ _e"> </span>|<span class="_ _e"> </span>sudo<span class="_ _e"> </span>tee<span class="_ _7"> </span>/proc/sys/vm/drop<span class="_ _11"> </span>caches;<span class="_ _e"> </span>sync</div><div class="t m0 x10 hc y194 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffc">CPU<span class="_ _d"> </span>isolation</span></div><div class="t m0 x6 hc y195 ff4 fs7 fc0 sc0 ls0 ws0">don’t<span class="_ _d"> </span>schedule<span class="_ _11"> </span>p<span class="_ _3"></span>ro<span class="_ _6"></span>cess<span class="_ _d"> </span>and<span class="_ _11"> </span>don’t<span class="_ _d"> </span>run<span class="_ _11"> </span>kernels<span class="_ _d"> </span>code<span class="_ _11"> </span>on<span class="_ _11"> </span>the<span class="_ _d"> </span>selected<span class="_ _11"> </span>CPUs.<span class="_ _10"> </span>GRUB</div><div class="t m0 x6 hc y196 ff4 fs7 fc0 sc0 ls0 ws0">options:<span class="_ _4"> </span><span class="ff6 fs4">isolcpus=<cpu<span class="_ _11"> </span>ids>,rcu<span class="_ _11"> </span>nocbs=<cpu<span class="_ _11"> </span>ids></span></div><div class="t m0 x12 hd y197 ffe fs6 fc7 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff9">How<span class="_ _a"> </span>to<span class="_ _10"> </span>get<span class="_ _a"> </span>consistent<span class="_ _a"> </span>results<span class="_ _10"> </span>when<span class="_ _a"> </span>benchmarking<span class="_ _a"> </span>on<span class="_ _10"> </span>Linux?</span></div><div class="t m0 x12 hd y198 ffe fs6 fc7 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff9">How<span class="_ _a"> </span>to<span class="_ _10"> </span>run<span class="_ _a"> </span>stable<span class="_ _a"> </span>benchmarks</span></div><div class="t m0 x12 hd y199 ffe fs6 fc7 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff9">Best<span class="_ _a"> </span>Practices<span class="_ _10"> </span>When<span class="_ _a"> </span>Benchmarking<span class="_ _a"> </span>CUDA<span class="_ _10"> </span>Applications</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">47/76</div><a class="l" href="https://easyperf.net/blog/2019/08/02/Perf-measurement-environment-on-Linux"><div class="d m1" style="border-style:none;position:absolute;left:49.168000px;bottom:38.452000px;width:270.311000px;height:9.365000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://archive.fosdem.org/2017/schedule/event/python_stable_benchmark/attachments/slides/1813/export/events/attachments/python_stable_benchmark/slides/1813/howto_run_stable_benchmarks.pdf"><div class="d m1" style="border-style:none;position:absolute;left:49.168000px;bottom:25.849000px;width:133.798000px;height:7.373000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://github.com/CppCon/CppCon2020/raw/main/Presentations/performance_matters/performance_matters__emery_berger__cppcon_2020.pdf"><div class="d m1" style="border-style:none;position:absolute;left:49.168000px;bottom:9.261000px;width:237.360000px;height:9.366000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf35" class="pf w0 h0" data-page-no="35"><div class="pc pc35 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Multi-Threads<span class="_ _1d"> </span>Considerations</div><div class="t m0 x10 hc y19a ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _1a"> </span><span class="ffd">numactl<span class="_ _7"> </span>--interleave=all</span></div><div class="t m0 x6 hc y19b ffd fs7 fc0 sc0 ls0 ws0">NUMA<span class="ff4">:<span class="_ _d"> </span>Non-Uniform<span class="_ _d"> </span>Memo<span class="_ _3"></span>ry<span class="_ _11"> </span>A<span class="_ _3"></span>ccess<span class="_ _11"> </span>(e.g.<span class="_ _10"> </span>multi-so<span class="_ _6"></span>ck<span class="_ _3"></span>et<span class="_ _d"> </span>system)</span></div><div class="t m0 x6 hc y19c ff4 fs7 fc0 sc0 ls0 ws0">The<span class="_ _d"> </span>default<span class="_ _11"> </span>b<span class="_ _6"></span>ehavio<span class="_ _3"></span>r<span class="_ _d"> </span>is<span class="_ _11"> </span>to<span class="_ _d"> </span>allo<span class="_ _6"></span>cate<span class="_ _d"> </span>memory<span class="_ _d"> </span>in<span class="_ _d"> </span>the<span class="_ _11"> </span>same<span class="_ _d"> </span>no<span class="_ _6"></span>de<span class="_ _d"> </span>as<span class="_ _11"> </span>a<span class="_ _11"> </span>thread<span class="_ _d"> </span>is</div><div class="t m0 x6 hc y19d ff4 fs7 fc0 sc0 ls0 ws0">scheduled<span class="_ _d"> </span>to<span class="_ _11"> </span>run<span class="_ _d"> </span>on,<span class="_ _11"> </span>and<span class="_ _d"> </span>this<span class="_ _11"> </span>wo<span class="_ _3"></span>rks<span class="_ _d"> </span>well<span class="_ _d"> </span>fo<span class="_ _3"></span>r<span class="_ _d"> </span>sma<span class="_ _6"></span>ll<span class="_ _d"> </span>amounts<span class="_ _d"> </span>of<span class="_ _11"> </span>memo<span class="_ _3"></span>ry<span class="_ _8"></span>.<span class="_ _10"> </span>How<span class="_ _3"></span>ever,</div><div class="t m0 x6 hc y19e ff4 fs7 fc0 sc0 ls0 ws0">when<span class="_ _d"> </span>you<span class="_ _d"> </span>w<span class="_ _3"></span>ant<span class="_ _11"> </span>to<span class="_ _d"> </span>allo<span class="_ _6"></span>cate<span class="_ _d"> </span>more<span class="_ _d"> </span>than<span class="_ _d"> </span>a<span class="_ _11"> </span>single<span class="_ _d"> </span>no<span class="_ _6"></span>de<span class="_ _d"> </span>memory<span class="_ _8"></span>,<span class="_ _d"> </span>it<span class="_ _d"> </span>is<span class="_ _11"> </span>no<span class="_ _11"> </span>longer</div><div class="t m0 x6 hc y19f ff4 fs7 fc0 sc0 ls0 ws0">p<span class="_ _6"></span>ossible.<span class="_ _a"> </span>This<span class="_ _11"> </span>option<span class="_ _d"> </span>sets<span class="_ _11"> </span>interleaved<span class="_ _d"> </span>memory<span class="_ _d"> </span>allo<span class="_ _6"></span>cations<span class="_ _d"> </span>among<span class="_ _11"> </span><span class="ffd">NUMA<span class="_ _d"> </span></span>no<span class="_ _6"></span>des</div><div class="t m0 x10 hc y1a0 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _1a"> </span><span class="ffd">export<span class="_ _7"> </span>OMP<span class="_ _9"> </span>NUM<span class="_ _9"> </span>THREADS=96<span class="_ _17"> </span><span class="ff4">Set<span class="_ _11"> </span>the<span class="_ _11"> </span>numb<span class="_ _6"></span>er<span class="_ _d"> </span>of<span class="_ _d"> </span>threads<span class="_ _11"> </span>in<span class="_ _d"> </span>an<span class="_ _11"> </span>Op<span class="_ _6"></span>enMP</span></span></div><div class="t m0 x6 hc y1a1 ff4 fs7 fc0 sc0 ls0 ws0">p<span class="_ _3"></span>rogram</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">48/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf36" class="pf w0 h0" data-page-no="36"><div class="pc pc36 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Program<span class="_ _1d"> </span>Memo<span class="_ _3"></span>ry<span class="_ _1d"> </span>Lay<span class="_ _3"></span>out</div><div class="t m0 x1 hc y16d ff4 fs7 fc0 sc0 ls0 ws0">A<span class="_ _d"> </span>small<span class="_ _11"> </span>co<span class="_ _6"></span>de<span class="_ _d"> </span>change<span class="_ _d"> </span>mo<span class="_ _6"></span>difies<span class="_ _d"> </span>the<span class="_ _11"> </span>memory<span class="_ _c"> </span>program<span class="_ _d"> </span>lay<span class="_ _3"></span>out</div><div class="t m0 x1 hc y16e ffa fs7 fc0 sc0 ls0 ws0">→<span class="_ _d"> </span><span class="ff4">large<span class="_ _d"> </span>impact<span class="_ _d"> </span>on<span class="_ _11"> </span>cache<span class="_ _d"> </span>(up<span class="_ _11"> </span>to<span class="_ _d"> </span>40%)</span></div><div class="t m0 x10 hc y16f ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Linking</span></div><div class="t m0 x11 hc y170 ff4 fs7 fc0 sc0 ls0 ws0">-<span class="_ _7"> </span>link<span class="_ _d"> </span>order<span class="_ _d"> </span><span class="ffa">→<span class="_ _d"> </span></span>changes<span class="_ _11"> </span>function<span class="_ _d"> </span>addresses</div><div class="t m0 x11 hc y171 ff4 fs7 fc0 sc0 ls0 ws0">-<span class="_ _7"> </span>upgrade<span class="_ _d"> </span>a<span class="_ _11"> </span>libra<span class="_ _3"></span>ry</div><div class="t m0 x10 hc y1a2 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Environment<span class="_ _9"> </span>V<span class="_ _3"></span>ariable<span class="_ _11"> </span>Size<span class="ff4">:<span class="_ _10"> </span>moves<span class="_ _d"> </span>the<span class="_ _11"> </span>p<span class="_ _3"></span>rogram<span class="_ _11"> </span>stack</span></span></div><div class="t m0 x11 hc y1a3 ff4 fs7 fc0 sc0 ls0 ws0">-<span class="_ _7"> </span>run<span class="_ _d"> </span>in<span class="_ _11"> </span>a<span class="_ _d"> </span>new<span class="_ _11"> </span>directory</div><div class="t m0 x11 hc y1a4 ff4 fs7 fc0 sc0 ls0 ws0">-<span class="_ _7"> </span>change<span class="_ _d"> </span>username</div><div class="t m0 x1 hd y1a5 ffe fs6 fc7 sc0 ls0 ws0">•<span class="ff9">Performance<span class="_ _a"> </span>Matters<span class="fff">,<span class="_ _c"> </span><span class="ff14">E.<span class="_ _c"> </span>Berger</span>,<span class="_ _c"> </span>CppCon20</span></span></div><div class="t m0 x1 hd y1a6 ffe fs6 fc7 sc0 ls0 ws0">•<span class="ff9">Producing<span class="_ _a"> </span>Wrong<span class="_ _a"> </span>Data<span class="_ _10"> </span>Without<span class="_ _a"> </span>Doing<span class="_ _10"> </span>Anything<span class="_ _a"> </span>Obviously<span class="_ _a"> </span>Wrong!<span class="fff">,<span class="_ _c"> </span><span class="ff14">Mytko<span class="_ _3"></span>wicz<span class="_ _c"> </span>et<span class="_ _c"> </span>al.<span class="fff">,</span></span></span></span></div><div class="t m0 x1 hd y1a7 fff fs6 fc7 sc0 ls0 ws0">ASPLOS’09</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">49/76</div><a class="l" href="https://github.com/CppCon/CppCon2020/raw/main/Presentations/performance_matters/performance_matters__emery_berger__cppcon_2020.pdf"><div class="d m1" style="border-style:none;position:absolute;left:34.916000px;bottom:32.296000px;width:178.986000px;height:11.407000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://dl.acm.org/doi/pdf/10.1145/1508284.1508275?casa_token=guQ1uetgcAgAAAAA:0APP42IvXLXit_o-Nx8XYoD5BkKHmdk1ISku2Vy5ZtCqkmbdQ8tCu3b8IjFWqxaWzknrsbrOFjdjCw"><div class="d m1" style="border-style:none;position:absolute;left:34.518000px;bottom:19.444000px;width:391.675000px;height:10.212000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://dl.acm.org/doi/pdf/10.1145/1508284.1508275?casa_token=guQ1uetgcAgAAAAA:0APP42IvXLXit_o-Nx8XYoD5BkKHmdk1ISku2Vy5ZtCqkmbdQ8tCu3b8IjFWqxaWzknrsbrOFjdjCw"><div class="d m1" style="border-style:none;position:absolute;left:27.350000px;bottom:5.053000px;width:47.890000px;height:12.000000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf37" class="pf w0 h0" data-page-no="37"><div class="pc pc37 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Measurement<span class="_ _1d"> </span>Overhead</div><div class="t m0 x1 hc y1a8 ff1 fs7 fc0 sc0 ls0 ws0">Time-measuring<span class="_ _11"> </span>functions<span class="_ _9"> </span>could<span class="_ _9"> </span>intro<span class="_ _6"></span>duce<span class="_ _9"> </span>significant<span class="_ _11"> </span>overhead<span class="_ _9"> </span>for<span class="_ _11"> </span>small</div><div class="t m0 x1 hc y1a9 ff1 fs7 fc0 sc0 ls0 ws0">computation</div><div class="t m0 xb hc y1aa ffd fs7 fc0 sc0 ls0 ws0">std::chrono::high<span class="_ _9"> </span>resolution<span class="_ _9"> </span>clock::now()<span class="_ _c"> </span><span class="ff4">/</span></div><div class="t m0 xb hc y1ab ffd fs7 fc0 sc0 ls0 ws0">std::chrono::system<span class="_ _9"> </span>clock::now()<span class="_ _17"> </span><span class="ff4">rely<span class="_ _d"> </span>on<span class="_ _11"> </span>libra<span class="_ _3"></span>ry/OS-p<span class="_ _3"></span>rovided<span class="_ _11"> </span>functions<span class="_ _d"> </span>to</span></div><div class="t m0 x1 hc y1ac ff4 fs7 fc0 sc0 ls0 ws0">retrieve<span class="_ _d"> </span>timestamps<span class="_ _11"> </span>(e.g.<span class="_ _4"> </span><span class="ffd">clock<span class="_ _9"> </span>gettime<span class="_ _c"> </span></span>)<span class="_ _d"> </span>and<span class="_ _11"> </span>their<span class="_ _d"> </span>execution<span class="_ _11"> </span>can<span class="_ _d"> </span>take<span class="_ _d"> </span>several<span class="_ _d"> </span>clo<span class="_ _6"></span>ck</div><div class="t m0 x1 hc y1ad ff4 fs7 fc0 sc0 ls0 ws0">cycles</div><div class="t m0 x1 hc y1ae ff4 fs7 fc0 sc0 ls0 ws0">Consider<span class="_ _d"> </span>using<span class="_ _11"> </span>a<span class="_ _d"> </span><span class="ff1">b<span class="_ _6"></span>enchma<span class="_ _3"></span>rking<span class="_ _9"> </span>framewo<span class="_ _3"></span>rk<span class="ff4">,<span class="_ _d"> </span>such<span class="_ _11"> </span>as<span class="_ _d"> </span><span class="ffd">Google<span class="_ _16"> </span>Benchmark<span class="_ _11"> </span></span>o<span class="_ _3"></span>r</span></span></div><div class="t m0 x1 hc y1af ffd fs7 fc0 sc0 ls0 ws0">nanobench<span class="_ _d"> </span><span class="ff4">(<span class="_ _c"> </span></span>std::chrono<span class="_ _17"> </span><span class="ff4">based),<span class="_ _d"> </span>to<span class="_ _11"> </span>retrieve<span class="_ _d"> </span>hardw<span class="_ _3"></span>a<span class="_ _3"></span>re<span class="_ _11"> </span>counters<span class="_ _d"> </span>and<span class="_ _11"> </span>get<span class="_ _d"> </span>basic</span></div><div class="t m0 x1 hc y1b0 ff4 fs7 fc0 sc0 ls0 ws0">p<span class="_ _3"></span>rofiling<span class="_ _11"> </span>info</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">50/76</div><a class="l" href="https://github.com/google/benchmark"><div class="d m1" style="border-style:none;position:absolute;left:274.744000px;bottom:69.107000px;width:93.629000px;height:11.993000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://nanobench.ankerl.com/"><div class="d m1" style="border-style:none;position:absolute;left:27.350000px;bottom:52.961000px;width:53.538000px;height:14.505000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf38" class="pf w0 h0" data-page-no="38"><div class="pc pc38 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA4wAAAH/CAIAAACTgcSzAAAACXBIWXMAABYlAAAWJQFJUiTwAAAIfklEQVR42u3ZoQ0CMQCG0ZZcg2QGgmICQtAktwKCeViCDRDMQIJgCDZAneKoKQ6DA0GPvDdB89d8aeNssQoAAFCN6+U8sgIAALURqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAvMScsxUAAKhHSslLKgAA1RGpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAQKQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVBMAACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAQKQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpJgAAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAMCfa4Zy0Hvft5utC4NhOR0PRgDgAzHnbAUAAOqRUvLdDwBAdUQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQBApAIAIFIBAOAbTbebWgEA3t3WeyPAT8yXbYyTcekeIZRSDAIAQBWeXIwdAl0DfAsAAAAASUVORK5CYII="/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Compiler<span class="_ _1d"> </span>Optimizations</div><div class="t m0 x1 hc y1b1 ff1 fs7 fc0 sc0 ls0 ws0">Compiler<span class="_ _11"> </span>optimizations<span class="_ _9"> </span>could<span class="_ _9"> </span>distort<span class="_ _11"> </span>the<span class="_ _9"> </span>actual<span class="_ _9"> </span>b<span class="_ _6"></span>enchma<span class="_ _3"></span>rk</div><div class="t m0 x10 hc y1b2 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffc">Dead<span class="_ _d"> </span>co<span class="_ _6"></span>de<span class="_ _d"> </span>elimination:<span class="_ _e"> </span><span class="ff4">the<span class="_ _11"> </span>compiler<span class="_ _d"> </span>discards<span class="_ _d"> </span>co<span class="_ _6"></span>de<span class="_ _d"> </span>that<span class="_ _d"> </span>do<span class="_ _6"></span>es<span class="_ _d"> </span>not<span class="_ _11"> </span>p<span class="_ _6"></span>erfo<span class="_ _3"></span>rm<span class="_ _d"> </span>“useful”</span></span></div><div class="t m0 x6 hc y1b3 ff4 fs7 fc0 sc0 ls0 ws0">computation</div><div class="t m0 x10 hc y1b4 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffc">Constant<span class="_ _d"> </span>propagation/Loop<span class="_ _11"> </span>optimization:<span class="_ _e"> </span><span class="ff4">the<span class="_ _d"> </span>compiler<span class="_ _11"> </span>is<span class="_ _d"> </span>able<span class="_ _11"> </span>to<span class="_ _d"> </span>pre-compute<span class="_ _d"> </span>the</span></span></div><div class="t m0 x6 hc y1b5 ff4 fs7 fc0 sc0 ls0 ws0">result<span class="_ _d"> </span>of<span class="_ _11"> </span>simple<span class="_ _d"> </span>co<span class="_ _6"></span>des</div><div class="t m0 x10 hc y1b6 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffc">Instruction<span class="_ _d"> </span>order:<span class="_ _10"> </span><span class="ff4">the<span class="_ _d"> </span>compiler<span class="_ _11"> </span>can<span class="_ _11"> </span>even<span class="_ _d"> </span>move<span class="_ _11"> </span>the<span class="_ _d"> </span>time-measuring<span class="_ _11"> </span>functions</span></span></div><div class="t m0 x10 ha y1b7 ff9 fs6 fc7 sc0 ls0 ws0">Microbenchmarking<span class="_ _a"> </span>Is<span class="_ _a"> </span>Tricky</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">51/76</div><a class="l" href="https://lucisqr.substack.com/p/microbenchmarking-is-tricky"><div class="d m1" style="border-style:none;position:absolute;left:34.722000px;bottom:4.635000px;width:129.091000px;height:13.444000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
|
||
<div id="pf39" class="pf w0 h0" data-page-no="39"><div class="pc pc39 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA4wAAAH/CAIAAACTgcSzAAAACXBIWXMAABYlAAAWJQFJUiTwAAAIhUlEQVR42u3ZMQ4BURSG0XkyL0prENXUChFRKIjYmE3Yi4LCDjR2oNKYeM3oLEBELjlnBTd/XvFlJo0mswoAAMK4nI49KwAAEI1IBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQDgJZVSrAAAQBw5Z19SAQAIR6QCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAQKQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSTQAAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAACBSAQAQqQAAIFIBAPhB9a8c2swXcY45H/ZBLrm37Xi18Y49DAD4M6mUYgUAAOLIOfvdDwBAOCIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAQKQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRagIAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAL6lvm2HVgCA91yXOyPAxzXTdUqDfnd7VFXXdQYBACCEJ6RUIAJfJAPRAAAAAElFTkSuQmCC"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Other<span class="_ _1d"> </span>Considerations</div><div class="t m0 x1 hc y1b8 ff1 fs7 fc0 sc0 ls0 ws0">The<span class="_ _11"> </span>actual<span class="_ _9"> </span>values<span class="_ _9"> </span>for<span class="_ _11"> </span>a<span class="_ _9"> </span>b<span class="_ _6"></span>enchma<span class="_ _3"></span>rk<span class="_ _9"> </span>could<span class="_ _11"> </span>significantly<span class="_ _9"> </span>affect<span class="_ _9"> </span>the<span class="_ _9"> </span>results<span class="ff4">.<span class="_ _10"> </span>F<span class="_ _3"></span>or</span></div><div class="t m0 x1 hc y1b9 ff4 fs7 fc0 sc0 ls0 ws0">instance,<span class="_ _d"> </span>a<span class="_ _11"> </span>GEMM<span class="_ _d"> </span>op<span class="_ _6"></span>eration<span class="_ _d"> </span>could<span class="_ _11"> </span>sho<span class="_ _3"></span>w<span class="_ _11"> </span>2X<span class="_ _d"> </span>p<span class="_ _6"></span>erformance<span class="_ _d"> </span>betw<span class="_ _3"></span>een<span class="_ _11"> </span>matrices<span class="_ _d"> </span>filled<span class="_ _11"> </span>with</div><div class="t m0 x1 hc y1ba ff4 fs7 fc0 sc0 ls0 ws0">zeros<span class="_ _d"> </span>and<span class="_ _11"> </span>random<span class="_ _d"> </span>values<span class="_ _11"> </span>due<span class="_ _d"> </span>to<span class="_ _11"> </span>the<span class="_ _d"> </span>effect<span class="_ _11"> </span>on<span class="_ _11"> </span>p<span class="_ _6"></span>o<span class="_ _3"></span>w<span class="_ _3"></span>er<span class="_ _11"> </span>consumption</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">52/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
|
||
<div id="pf3a" class="pf w0 h0" data-page-no="3a"><div class="pc pc3a w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Metric<span class="_ _1d"> </span>Evaluation<span class="_ _2e"> </span>1/6</div><div class="t m0 x1 hc y1bb ff4 fs7 fc0 sc0 ls0 ws0">After<span class="_ _d"> </span>extracting<span class="_ _11"> </span>and<span class="_ _d"> </span>collecting<span class="_ _11"> </span>p<span class="_ _6"></span>erfo<span class="_ _3"></span>rmance<span class="_ _d"> </span>results,<span class="_ _11"> </span>it<span class="_ _d"> </span>is<span class="_ _11"> </span>fundamental<span class="_ _11"> </span>to</div><div class="t m0 x1 hc y1bc ff4 fs7 fc0 sc0 ls0 ws0">rep<span class="_ _6"></span>o<span class="_ _3"></span>rt/summa<span class="_ _3"></span>rize<span class="_ _11"> </span>them<span class="_ _d"> </span>in<span class="_ _11"> </span>a<span class="_ _d"> </span>wa<span class="_ _3"></span>y<span class="_ _11"> </span>to<span class="_ _d"> </span>fully<span class="_ _11"> </span>understand<span class="_ _d"> </span>the<span class="_ _11"> </span>exp<span class="_ _6"></span>eriment,<span class="_ _d"> </span>p<span class="_ _3"></span>rovide</div><div class="t m0 x1 hc y1bd ff4 fs7 fc0 sc0 ls0 ws0">interp<span class="_ _3"></span>retable<span class="_ _11"> </span>insights,<span class="_ _d"> </span>ensure<span class="_ _11"> </span>reliability<span class="_ _5"></span>,<span class="_ _11"> </span>and<span class="_ _11"> </span>compa<span class="_ _3"></span>re<span class="_ _11"> </span>different<span class="_ _d"> </span>observations,<span class="_ _11"> </span>e.g.<span class="_ _10"> </span>co<span class="_ _6"></span>des,</div><div class="t m0 x1 hc y1be ff4 fs7 fc0 sc0 ls0 ws0">algo<span class="_ _3"></span>rithms,<span class="_ _11"> </span>systems,<span class="_ _d"> </span>etc.</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">53/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
|
||
<div id="pf3b" class="pf w0 h0" data-page-no="3b"><div class="pc pc3b w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Metric<span class="_ _1d"> </span>Evaluation<span class="_ _2e"> </span>2/6</div><div class="t m0 x28 hd y1bf ff1 fs6 fc0 sc0 ls0 ws0">Metric<span class="_ _2f"> </span>F<span class="_ _3"></span>ormula<span class="_ _30"> </span>Description</div><div class="t m0 x33 hd y1c0 ff1 fs6 fc0 sc0 ls0 ws0">Arithmetic<span class="_ _c"> </span>mean<span class="_ _31"> </span><span class="fff">¯<span class="_ _32"></span><span class="ff14">x<span class="_ _d"> </span><span class="fff">=</span></span></span></div><div class="t m0 x34 h11 y1c1 ff15 fs8 fc0 sc0 ls0 ws0">n</div><div class="t m0 x35 h12 y1c2 ff16 fs4 fc0 sc0 ls0 ws0">P</div><div class="t m0 x35 h13 y1c3 ff15 fs8 fc0 sc0 ls0 ws0">i<span class="_ _6"> </span><span class="ff5">=1</span></div><div class="t m0 x36 h14 y1c0 ff14 fs6 fc0 sc0 ls0 ws0">x</div><div class="t m0 x37 h11 y1c4 ff15 fs8 fc0 sc0 ls0 ws0">i</div><div class="t m0 x38 hd y1c0 fff fs6 fc0 sc0 ls0 ws0">F<span class="_ _3"></span>or<span class="_ _21"> </span>sum<span class="_ _6"></span>ma<span class="_ _3"></span>rizing<span class="_ _c"> </span>costs,<span class="_ _21"> </span>e.g.<span class="_ _1d"> </span>exec.<span class="_ _9"> </span>times,<span class="_ _c"> </span>floating<span class="_ _c"> </span>point<span class="_ _c"> </span>ops,<span class="_ _c"> </span>etc.</div><div class="t m0 x33 hd y1c5 ff1 fs6 fc0 sc0 ls0 ws0">Ha<span class="_ _3"></span>rmonic<span class="_ _d"> </span>mean</div><div class="t m0 x39 h14 y1c6 ff14 fs6 fc0 sc0 ls0 ws0">n</div><div class="t m0 x3a h11 y1c7 ff15 fs8 fc0 sc0 ls0 ws0">n</div><div class="t m0 x30 h12 y1c8 ff16 fs4 fc0 sc0 ls0 ws0">P</div><div class="t m0 x30 h13 y1c9 ff15 fs8 fc0 sc0 ls0 ws0">i<span class="_ _6"> </span><span class="ff5">=1</span></div><div class="t m0 x34 hd y1ca fff fs6 fc0 sc0 ls0 ws0">1</div><div class="t m0 x34 h14 y1cb ff14 fs6 fc0 sc0 ls0 ws0">x</div><div class="t m0 x3b h11 y1cc ff15 fs8 fc0 sc0 ls0 ws0">i</div><div class="t m0 x38 hd y1c5 fff fs6 fc0 sc0 ls0 ws0">F<span class="_ _3"></span>or<span class="_ _21"> </span>sum<span class="_ _6"></span>ma<span class="_ _3"></span>rizing<span class="_ _c"> </span>rates,<span class="_ _21"> </span>e.g.<span class="_ _1d"> </span>flop/s</div><div class="t m0 x33 hd y1cd ff1 fs6 fc0 sc0 ls0 ws0">Geometric<span class="_ _c"> </span>mean</div><div class="t m0 x30 h15 y1ce ff15 fs9 fc0 sc0 ls0 ws0">n</div><div class="t m0 x3c h12 y1cf ff16 fs4 fc0 sc0 ls0 ws0">r</div><div class="t m0 x35 h11 y1d0 ff15 fs8 fc0 sc0 ls0 ws0">n</div><div class="t m0 x3d h12 y1d1 ff16 fs4 fc0 sc0 ls0 ws0">Q</div><div class="t m0 x3d h13 y1d2 ff15 fs8 fc0 sc0 ls0 ws0">i<span class="_ _6"> </span><span class="ff5">=1</span></div><div class="t m0 x3b h14 y1cd ff14 fs6 fc0 sc0 ls0 ws0">x</div><div class="t m0 x36 h11 y1d3 ff15 fs8 fc0 sc0 ls0 ws0">i</div><div class="t m0 x38 hd y1d4 fff fs6 fc0 sc0 ls0 ws0">F<span class="_ _3"></span>or<span class="_ _21"> </span>sum<span class="_ _6"></span>ma<span class="_ _3"></span>rizing<span class="_ _c"> </span>rates.<span class="_ _9"> </span>Ha<span class="_ _3"></span>rmonic<span class="_ _c"> </span>mean<span class="_ _c"> </span>should<span class="_ _c"> </span>be<span class="_ _c"> </span>preferred.</div><div class="t m0 x38 hd y1d5 fff fs6 fc0 sc0 ls0 ws0">Commonly<span class="_ _c"> </span>used<span class="_ _21"> </span>for<span class="_ _21"> </span>comparing<span class="_ _c"> </span>speedup</div><div class="t m0 x33 hd y1d6 ff1 fs6 fc0 sc0 ls0 ws0">Standa<span class="_ _3"></span>rd<span class="_ _d"> </span>deviation</div><div class="t m0 x3e hd y1d7 ff17 fs6 fc0 sc0 ls0 ws0">σ<span class="_ _21"> </span><span class="fff">=</span></div><div class="t m0 x3d h15 y1d8 ff15 fs9 fc0 sc0 ls0 ws0">n</div><div class="t m0 x3f h12 y1d9 ff16 fs4 fc0 sc0 ls0 ws0">P</div><div class="t m0 x3a h16 y1da ff15 fs9 fc0 sc0 ls0 ws0">i<span class="_ _6"> </span><span class="ff5">=1</span></div><div class="t m0 x34 h13 y1db ff5 fs8 fc0 sc0 ls0 ws0">(<span class="ff15">x</span></div><div class="t m0 x40 h15 y1dc ff15 fs9 fc0 sc0 ls0 ws0">i</div><div class="t m0 x36 h13 y1db ff18 fs8 fc0 sc0 ls0 ws0">−<span class="ff15">x<span class="_ _6"> </span><span class="ff5">)</span></span></div><div class="t m0 x41 h16 y1dd ff5 fs9 fc0 sc0 ls0 ws0">2</div><div class="t m0 x34 h13 y1de ff15 fs8 fc0 sc0 ls0 ws0">n<span class="ff18">−<span class="ff5">1</span></span></div><div class="t m0 x38 hd y1d6 fff fs6 fc0 sc0 ls0 ws0">Measure<span class="_ _c"> </span>of<span class="_ _21"> </span>the<span class="_ _c"> </span>spread<span class="_ _21"> </span>of<span class="_ _c"> </span>normally<span class="_ _21"> </span>distributed<span class="_ _c"> </span>samples</div><div class="t m0 x33 hd y1df ff1 fs6 fc0 sc0 ls0 ws0">Co<span class="_ _6"></span>efficient<span class="_ _c"> </span>of</div><div class="t m0 x33 hd y1e0 ff1 fs6 fc0 sc0 ls0 ws0">V<span class="_ _3"></span>ariation</div><div class="t m0 x3c hd y1e1 ff14 fs6 fc0 sc0 ls0 ws0">std<span class="_ _0"></span><span class="ff17">.</span>dev</div><div class="t m0 x27 hd y1e2 ff14 fs6 fc0 sc0 ls0 ws0">a<span class="_ _3"></span>rith<span class="_ _6"></span><span class="ff17">.</span>mean</div><div class="t m0 x38 hd y1df fff fs6 fc0 sc0 ls0 ws0">Rep<span class="_ _3"></span>resents<span class="_ _c"> </span>the<span class="_ _c"> </span>stability<span class="_ _21"> </span>of<span class="_ _c"> </span>a<span class="_ _c"> </span>set<span class="_ _c"> </span>of<span class="_ _21"> </span>normally<span class="_ _21"> </span>distributed</div><div class="t m0 x38 hd y1e0 fff fs6 fc0 sc0 ls0 ws0">measurement<span class="_ _c"> </span>results.<span class="_ _9"> </span>No<span class="_ _3"></span>rmalized<span class="_ _c"> </span>standard<span class="_ _21"> </span>deviation</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">54/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf3c" class="pf w0 h0" data-page-no="3c"><div class="pc pc3c w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Metric<span class="_ _1d"> </span>Evaluation<span class="_ _2e"> </span>3/6</div><div class="t m0 x6 hd y1bf ff1 fs6 fc0 sc0 ls0 ws0">Metric<span class="_ _33"> </span>F<span class="_ _3"></span>ormula<span class="_ _34"> </span>Description</div><div class="t m0 x33 hd y1e3 ff1 fs6 fc0 sc0 ls0 ws0">Confidence<span class="_ _21"> </span>intervals</div><div class="t m0 x33 hd y1e4 ff1 fs6 fc0 sc0 ls0 ws0">of<span class="_ _c"> </span>the<span class="_ _d"> </span>mean</div><div class="t m0 x30 hd y1e5 ff14 fs6 fc0 sc0 ls0 ws0">z<span class="_ _c"> </span><span class="fff">=<span class="_ _21"> </span></span>t</div><div class="t m0 x36 h12 y1e6 ff16 fs4 fc0 sc0 ls0 ws0"></div><div class="t m0 x37 hd y1e5 ff14 fs6 fc0 sc0 ls0 ws0">n<span class="_ _35"> </span><span class="ff19">−<span class="_ _35"> </span><span class="fff">1<span class="ff17">,</span></span></span></div><div class="t m0 x42 h17 y1e7 ff1a fs8 fc0 sc0 ls0 ws0">α</div><div class="t m0 x42 h13 y1e8 ff5 fs8 fc0 sc0 ls0 ws0">2</div><div class="t m0 x43 h12 y1e6 ff16 fs4 fc0 sc0 ls0 ws0"></div><div class="t m0 x25 hd y1e9 ff14 fs6 fc0 sc0 ls0 ws0">CI<span class="_ _c"> </span><span class="fff">=</span></div><div class="t m0 x3d h12 y1ea ff16 fs4 fc0 sc0 ls0 ws0">h</div><div class="t m0 x34 hd y1e9 fff fs6 fc0 sc0 ls0 ws0">¯<span class="_ _32"></span><span class="ff14">x<span class="_ _21"> </span><span class="ff19">−</span></span></div><div class="t m0 x44 h11 y1eb ff15 fs8 fc0 sc0 ls0 ws0">z<span class="_ _6"> </span><span class="ff1a">σ</span></div><div class="t m0 x44 h18 y1ec ff18 fs8 fc0 sc0 ls0 ws0">√</div><div class="t m0 x45 h11 y1ed ff15 fs8 fc0 sc0 ls0 ws0">n</div><div class="t m0 x46 hd y1e9 ff17 fs6 fc0 sc0 ls0 ws0">,<span class="_ _36"> </span><span class="fff">¯<span class="_ _32"></span><span class="ff14">x<span class="_ _21"> </span><span class="fff">+</span></span></span></div><div class="t m0 x47 h11 y1eb ff15 fs8 fc0 sc0 ls0 ws0">z<span class="_ _6"> </span><span class="ff1a">σ</span></div><div class="t m0 x47 h18 y1ec ff18 fs8 fc0 sc0 ls0 ws0">√</div><div class="t m0 x48 h11 y1ed ff15 fs8 fc0 sc0 ls0 ws0">n</div><div class="t m0 x49 h12 y1ea ff16 fs4 fc0 sc0 ls0 ws0">i</div><div class="t m0 x4a hd y1c0 fff fs6 fc0 sc0 ls0 ws0">Measure<span class="_ _c"> </span>of<span class="_ _21"> </span>reliability<span class="_ _21"> </span>of<span class="_ _c"> </span>the<span class="_ _c"> </span>exp<span class="_ _6"></span>eriment.<span class="_ _11"> </span>The<span class="_ _c"> </span>concept</div><div class="t m0 x4a hd y1e3 fff fs6 fc0 sc0 ls0 ws0">is<span class="_ _c"> </span>interp<span class="_ _3"></span>reted<span class="_ _c"> </span>as<span class="_ _c"> </span>the<span class="_ _c"> </span>p<span class="_ _3"></span>robability<span class="_ _21"> </span>(e.g.<span class="_ _9"> </span><span class="ff17">α<span class="_ _21"> </span></span>=<span class="_ _21"> </span>95%)<span class="_ _21"> </span>that</div><div class="t m0 x4a hd y1e4 fff fs6 fc0 sc0 ls0 ws0">the<span class="_ _c"> </span>observed<span class="_ _21"> </span>confidential<span class="_ _c"> </span>interval<span class="_ _c"> </span>contains<span class="_ _c"> </span>the<span class="_ _c"> </span>true</div><div class="t m0 x4a hd y1ee fff fs6 fc0 sc0 ls0 ws0">mean</div><div class="t m0 x33 hd y1ef ff1 fs6 fc0 sc0 ls0 ws0">Median</div><div class="t m0 x4b hd y1f0 fff fs6 fc0 sc0 ls0 ws0">value<span class="_ _c"> </span>at<span class="_ _21"> </span>p<span class="_ _6"></span>osition<span class="_ _c"> </span><span class="ff14">n<span class="ff17">/</span></span>2</div><div class="t m0 x23 hd y1f1 fff fs6 fc0 sc0 ls0 ws0">after<span class="_ _c"> </span>so<span class="_ _3"></span>rting<span class="_ _c"> </span>all<span class="_ _c"> </span>data</div><div class="t m0 x4a hd y1f0 fff fs6 fc0 sc0 ls0 ws0">Rank<span class="_ _c"> </span>measures<span class="_ _21"> </span>are<span class="_ _21"> </span>more<span class="_ _c"> </span>robust<span class="_ _21"> </span>with<span class="_ _c"> </span>regard<span class="_ _21"> </span>to</div><div class="t m0 x4a hd y1f1 fff fs6 fc0 sc0 ls0 ws0">outliers<span class="_ _c"> </span>but<span class="_ _21"> </span>do<span class="_ _c"> </span>not<span class="_ _c"> </span>consider<span class="_ _c"> </span>all<span class="_ _c"> </span>measured<span class="_ _21"> </span>values</div><div class="t m0 x33 hd y1f2 ff1 fs6 fc0 sc0 ls0 ws0">Quantile:</div><div class="t m0 x33 hd y1f3 ff1 fs6 fc0 sc0 ls0 ws0">P<span class="_ _3"></span>ercentile/Quartile</div><div class="t m0 x4c hd y1f2 fff fs6 fc0 sc0 ls0 ws0">value<span class="_ _21"> </span>at<span class="_ _21"> </span>a<span class="_ _c"> </span>given<span class="_ _21"> </span>position</div><div class="t m0 x23 hd y1f3 fff fs6 fc0 sc0 ls0 ws0">after<span class="_ _c"> </span>so<span class="_ _3"></span>rting<span class="_ _c"> </span>all<span class="_ _c"> </span>data</div><div class="t m0 x4a hd y1f4 fff fs6 fc0 sc0 ls0 ws0">The<span class="_ _c"> </span>percentiles/quartiles<span class="_ _21"> </span>provide<span class="_ _21"> </span>information<span class="_ _c"> </span>about</div><div class="t m0 x4a hd y1f5 fff fs6 fc0 sc0 ls0 ws0">the<span class="_ _c"> </span>sp<span class="_ _3"></span>read<span class="_ _c"> </span>of<span class="_ _c"> </span>the<span class="_ _c"> </span>data<span class="_ _21"> </span>and<span class="_ _c"> </span>the<span class="_ _c"> </span>skew.<span class="_ _11"> </span>It<span class="_ _c"> </span>indicates<span class="_ _c"> </span>the</div><div class="t m0 x4a hd y1f6 fff fs6 fc0 sc0 ls0 ws0">value<span class="_ _c"> </span>below<span class="_ _21"> </span>which<span class="_ _c"> </span>a<span class="_ _c"> </span>given<span class="_ _c"> </span>p<span class="_ _6"></span>ercentage<span class="_ _21"> </span>of<span class="_ _c"> </span>data<span class="_ _c"> </span>falls</div><div class="t m0 x33 hd y1f7 ff1 fs6 fc0 sc0 ls0 ws0">Minumum/</div><div class="t m0 x33 hd y1f8 ff1 fs6 fc0 sc0 ls0 ws0">Maximum</div><div class="t m0 x30 hd y1f9 fff fs6 fc0 sc0 ls0 ws0">min<span class="_ _37"> </span><span class="ff17">/<span class="_ _37"> </span></span>max</div><div class="t m0 x4d h11 y1fa ff15 fs8 fc0 sc0 ls0 ws0">n</div><div class="t m0 x4d h13 y1fb ff15 fs8 fc0 sc0 ls0 ws0">i<span class="_ _6"> </span><span class="ff5">=1</span></div><div class="t m0 x38 hd y1f9 fff fs6 fc0 sc0 ls0 ws0">(<span class="ff14">x</span></div><div class="t m0 x4e h11 y1fc ff15 fs8 fc0 sc0 ls0 ws0">i</div><div class="t m0 x4f hd y1f9 fff fs6 fc0 sc0 ls0 ws0">)</div><div class="t m0 x4a hd y1f7 fff fs6 fc0 sc0 ls0 ws0">Provide<span class="_ _c"> </span>the<span class="_ _21"> </span>low<span class="_ _3"></span>er/upp<span class="_ _6"></span>er<span class="_ _c"> </span>bounds<span class="_ _c"> </span>of<span class="_ _c"> </span>the<span class="_ _c"> </span>data,<span class="_ _21"> </span>namely</div><div class="t m0 x4a hd y1f8 fff fs6 fc0 sc0 ls0 ws0">the<span class="_ _c"> </span>range<span class="_ _21"> </span>of<span class="_ _c"> </span>the<span class="_ _c"> </span>values</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">55/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
|
||
<div id="pf3d" class="pf w0 h0" data-page-no="3d"><div class="pc pc3d w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Metric<span class="_ _1d"> </span>Evaluation<span class="_ _2e"> </span>4/6</div><div class="t m0 x36 hd y1fd ff1 fs6 fc0 sc0 ls0 ws0">Confidence<span class="_ _c"> </span>Interval<span class="_ _38"> </span>Z</div><div class="t m0 x44 hd y1fe fff fs6 fc0 sc0 ls0 ws0">80%<span class="_ _39"> </span>1.282</div><div class="t m0 x44 hd y1ff fff fs6 fc0 sc0 ls0 ws0">85%<span class="_ _39"> </span>1.440</div><div class="t m0 x44 hd y200 fff fs6 fc0 sc0 ls0 ws0">90%<span class="_ _39"> </span>1.645</div><div class="t m0 x44 hd y201 fff fs6 fc0 sc0 ls0 ws0">95%<span class="_ _39"> </span>1.960</div><div class="t m0 x44 hd y202 fff fs6 fc0 sc0 ls0 ws0">99%<span class="_ _39"> </span>2.576</div><div class="t m0 x44 hd y203 fff fs6 fc0 sc0 ls0 ws0">99.5%<span class="_ _3a"> </span>2.807</div><div class="t m0 x44 hd y204 fff fs6 fc0 sc0 ls0 ws0">99.9%<span class="_ _3a"> </span>3.291</div><div class="t m0 x1 hc y205 ff4 fs7 fc0 sc0 ls0 ws0">Some<span class="_ _d"> </span>metrics<span class="_ _11"> </span>assume<span class="_ _d"> </span>a<span class="_ _11"> </span>normal<span class="_ _c"> </span>distribution<span class="_ _11"> </span><span class="ffa">→<span class="_ _11"> </span></span>the<span class="_ _d"> </span>arithmetic<span class="_ _d"> </span>mean,<span class="_ _d"> </span>median<span class="_ _11"> </span>and<span class="_ _d"> </span>mo<span class="_ _6"></span>de</div><div class="t m0 x1 hc y206 ff4 fs7 fc0 sc0 ls0 ws0">a<span class="_ _3"></span>re<span class="_ _11"> </span>all<span class="_ _d"> </span>equal</div><div class="t m0 x50 hc y207 ffa fs7 fc0 sc0 ls0 ws0">|<span class="_ _6"></span><span class="ff4">¯<span class="_ _3b"></span><span class="ffc">x<span class="_ _d"> </span><span class="ffa">−<span class="_ _3c"> </span></span>median<span class="ffa">|</span></span></span></div><div class="t m0 x4e hc y208 ff4 fs7 fc0 sc0 ls0 ws0">max<span class="_ _35"> </span>(<span class="_ _6"></span>¯<span class="_ _3b"></span><span class="ffc">x<span class="_ _15"></span><span class="ff1b">,<span class="_ _36"> </span></span>median<span class="ff4">)</span></span></div><div class="t m0 x1 hc y209 ff4 fs7 fc0 sc0 ls0 ws0">If<span class="_ _d"> </span>the<span class="_ _11"> </span><span class="ffc">relative<span class="_ _d"> </span>difference<span class="_ _11"> </span>b<span class="_ _6"></span>et<span class="_ _3"></span>ween<span class="_ _d"> </span>the<span class="_ _d"> </span>mean<span class="_ _d"> </span>and<span class="_ _11"> </span>median<span class="_ _11"> </span><span class="ff4">is<span class="_ _11"> </span>la<span class="_ _3"></span>rger<span class="_ _11"> </span>than<span class="_ _d"> </span>1%,<span class="_ _11"> </span>values<span class="_ _d"> </span>are</span></span></div><div class="t m0 x1 hc y20a ff4 fs7 fc0 sc0 ls0 ws0">p<span class="_ _3"></span>robably<span class="_ _11"> </span>not<span class="_ _d"> </span>normally<span class="_ _d"> </span>distributed</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">56/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf3e" class="pf w0 h0" data-page-no="3e"><div class="pc pc3e w0 h0"><img class="bi x0 y0 w1 h1" alt="" 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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Metric<span class="_ _1d"> </span>Evaluation<span class="_ _2e"> </span>5/6</div><div class="t m0 x1 hc y48 ff1 fs7 fc0 sc0 ls0 ws0">Minimum/Maximum<span class="_ _11"> </span>vs.<span class="_ _e"> </span>Arithmetic<span class="_ _11"> </span>mean.<span class="_ _10"> </span><span class="ff4">The<span class="_ _d"> </span>minimum/maximum<span class="_ _d"> </span>could<span class="_ _d"> </span>b<span class="_ _6"></span>e<span class="_ _d"> </span>used</span></div><div class="t m0 x1 hc y72 ff4 fs7 fc0 sc0 ls0 ws0">to<span class="_ _d"> </span>get<span class="_ _11"> </span>the<span class="_ _d"> </span>b<span class="_ _6"></span>est<span class="_ _d"> </span>outcome<span class="_ _11"> </span>of<span class="_ _d"> </span>an<span class="_ _11"> </span>exp<span class="_ _6"></span>eriment,<span class="_ _d"> </span>namely<span class="_ _d"> </span>the<span class="_ _11"> </span>measure<span class="_ _d"> </span>with<span class="_ _11"> </span>the<span class="_ _11"> </span>least<span class="_ _d"> </span>noise.</div><div class="t m0 x1 hc yc4 ff4 fs7 fc0 sc0 ls0 ws0">On<span class="_ _d"> </span>the<span class="_ _11"> </span>other<span class="_ _d"> </span>hand,<span class="_ _11"> </span>the<span class="_ _d"> </span>arithmetic<span class="_ _d"> </span>mean<span class="_ _d"> </span>considers<span class="_ _11"> </span>all<span class="_ _d"> </span>values<span class="_ _11"> </span>and<span class="_ _d"> </span>could<span class="_ _11"> </span>b<span class="_ _6"></span>etter<span class="_ _d"> </span>represent</div><div class="t m0 x1 hc y20b ff4 fs7 fc0 sc0 ls0 ws0">the<span class="_ _d"> </span>b<span class="_ _6"></span>ehavio<span class="_ _3"></span>r<span class="_ _11"> </span>of<span class="_ _d"> </span>the<span class="_ _11"> </span>exp<span class="_ _6"></span>eriment.</div><div class="t m0 x1 hc y20c ff4 fs7 fc0 sc0 ls0 ws0">If<span class="_ _d"> </span>the<span class="_ _11"> </span><span class="ffc">sk<span class="_ _3"></span>ewness<span class="_ _a"> </span><span class="ff4">of<span class="_ _d"> </span>the<span class="_ _11"> </span>distribution<span class="_ _11"> </span>is<span class="_ _d"> </span></span>symmetrical<span class="_ _a"> </span><span class="ff4">(e.g.<span class="_ _10"> </span>normal,<span class="_ _d"> </span>binomial)<span class="_ _d"> </span>then<span class="_ _11"> </span>the</span></span></div><div class="t m0 x1 hc y20d ff4 fs7 fc0 sc0 ls0 ws0">a<span class="_ _3"></span>rithmetic<span class="_ _11"> </span>mean<span class="_ _d"> </span>is<span class="_ _11"> </span>a<span class="_ _d"> </span>sup<span class="_ _6"></span>erior<span class="_ _d"> </span>statistic,<span class="_ _d"> </span>while<span class="_ _d"> </span>the<span class="_ _11"> </span>minimum/maximum<span class="_ _d"> </span>could<span class="_ _11"> </span>b<span class="_ _6"></span>e<span class="_ _d"> </span>useful</div><div class="t m0 x1 hc y20e ff4 fs7 fc0 sc0 ls0 ws0">in<span class="_ _d"> </span>the<span class="_ _11"> </span>opp<span class="_ _6"></span>osite<span class="_ _d"> </span>case<span class="_ _d"> </span>(e.g.<span class="_ _10"> </span>log-normal<span class="_ _d"> </span>distribution)</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">57/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Metric<span class="_ _1d"> </span>Evaluation<span class="_ _2e"> </span>6/6</div><div class="t m0 x12 hd y20f ffe fs6 fc7 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff9">Benchmarking:<span class="_ _12"> </span>minimum<span class="_ _a"> </span>vs<span class="_ _10"> </span>average</span></div><div class="t m0 x12 hd y210 ffe fs6 fc7 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff9">Scientific<span class="_ _a"> </span>Benchmarking<span class="_ _10"> </span>of<span class="_ _a"> </span>Parallel<span class="_ _a"> </span>Computing<span class="_ _10"> </span>Systems</span></div><div class="t m0 x12 hd y211 ffe fs6 fc7 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff9">Benchmarking<span class="_ _a"> </span>C++<span class="_ _10"> </span>Code</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">58/76</div><a class="l" href="https://blog.kevmod.com/2016/06/10/benchmarking-minimum-vs-average/"><div class="d m1" style="border-style:none;position:absolute;left:49.168000px;bottom:32.204000px;width:157.335000px;height:9.365000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="http://htor.inf.ethz.ch/publications/img/hoefler-scientific-benchmarking.pdf"><div class="d m1" style="border-style:none;position:absolute;left:49.168000px;bottom:17.608000px;width:251.482000px;height:9.366000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://raw.githubusercontent.com/CppCon/CppCon2015/master/Presentations/Benchmarking%20C%2B%2B%20Code/Benchmarking%20C%2B%2B%20Code%20-%20Bryce%20Adelstein%20Lelbach%20-%20CppCon%202015.pdf"><div class="d m1" style="border-style:none;position:absolute;left:49.168000px;bottom:3.013000px;width:100.847000px;height:9.366000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf40" class="pf w0 h0" data-page-no="40"><div class="pc pc40 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x8 h2 yd4 ff1 fs0 fc0 sc0 ls0 ws0">Profiling</div><a class="l" href="#pf40" data-dest-detail='[64,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:105.775000px;bottom:132.432000px;width:97.010000px;height:24.026000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf41" class="pf w0 h0" data-page-no="41"><div class="pc pc41 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Overview</div><div class="t m0 x1 hc y212 ff4 fs7 fc0 sc0 ls0 ws0">A<span class="_ _d"> </span><span class="ff1">co<span class="_ _6"></span>de<span class="_ _9"> </span>p<span class="_ _3"></span>rofiler<span class="_ _11"> </span><span class="ff4">is<span class="_ _11"> </span>a<span class="_ _d"> </span>form<span class="_ _d"> </span>of<span class="_ _d"> </span><span class="ffc">dynamic<span class="_ _11"> </span>p<span class="_ _3"></span>rogram<span class="_ _11"> </span>analysis<span class="_ _1d"> </span><span class="ff4">which<span class="_ _11"> </span>aims<span class="_ _d"> </span>at<span class="_ _11"> </span>investigating<span class="_ _d"> </span>the</span></span></span></span></div><div class="t m0 x1 hc y213 ff4 fs7 fc0 sc0 ls0 ws0">p<span class="_ _3"></span>rogram<span class="_ _a"> </span>b<span class="_ _6"></span>ehavio<span class="_ _3"></span>r<span class="_ _a"> </span>to<span class="_ _a"> </span>find<span class="_ _a"> </span>p<span class="_ _6"></span>erfo<span class="_ _3"></span>rmance<span class="_ _d"> </span>b<span class="_ _6"></span>ottleneck.<span class="_ _4"> </span>A<span class="_ _9"> </span>profiler<span class="_ _1d"> </span>is<span class="_ _a"> </span>crucial<span class="_ _a"> </span>in<span class="_ _a"> </span>saving<span class="_ _a"> </span>time</div><div class="t m0 x1 hc y214 ff4 fs7 fc0 sc0 ls0 ws0">and<span class="_ _d"> </span>effort<span class="_ _d"> </span>during<span class="_ _d"> </span>the<span class="_ _11"> </span>development<span class="_ _d"> </span>and<span class="_ _11"> </span>optimization<span class="_ _d"> </span>process<span class="_ _11"> </span>of<span class="_ _d"> </span>an<span class="_ _11"> </span>application</div><div class="t m0 x1 hc y215 ff4 fs7 fc0 sc0 ls0 ws0">Co<span class="_ _6"></span>de<span class="_ _d"> </span>p<span class="_ _3"></span>rofilers<span class="_ _11"> </span>a<span class="_ _3"></span>re<span class="_ _11"> </span>generally<span class="_ _d"> </span>based<span class="_ _11"> </span>on<span class="_ _11"> </span>the<span class="_ _d"> </span>following<span class="_ _d"> </span>metho<span class="_ _6"></span>dologies:</div><div class="t m0 x10 hc y216 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Instrumentation<span class="_ _9"> </span><span class="ff4">Instrumenting<span class="_ _9"> </span>profilers<span class="_ _11"> </span>insert<span class="_ _9"> </span>sp<span class="_ _6"></span>ecial<span class="_ _11"> </span>co<span class="_ _6"></span>de<span class="_ _9"> </span>at<span class="_ _9"> </span>the<span class="_ _9"> </span>b<span class="_ _6"></span>eginning<span class="_ _9"> </span>and</span></span></div><div class="t m0 x6 hc y217 ff4 fs7 fc0 sc0 ls0 ws0">end<span class="_ _d"> </span>of<span class="_ _11"> </span>each<span class="_ _11"> </span>routine<span class="_ _d"> </span>to<span class="_ _11"> </span>record<span class="_ _d"> </span>when<span class="_ _d"> </span>the<span class="_ _11"> </span>routine<span class="_ _11"> </span>starts<span class="_ _d"> </span>and<span class="_ _d"> </span>when<span class="_ _11"> </span>it<span class="_ _d"> </span>exits.<span class="_ _e"> </span>With<span class="_ _11"> </span>this</div><div class="t m0 x6 hc y218 ff4 fs7 fc0 sc0 ls0 ws0">info<span class="_ _3"></span>rmation,<span class="_ _9"> </span>the<span class="_ _9"> </span>profiler<span class="_ _11"> </span>aims<span class="_ _9"> </span>to<span class="_ _9"> </span>measure<span class="_ _9"> </span>the<span class="_ _9"> </span>actual<span class="_ _9"> </span>time<span class="_ _9"> </span>tak<span class="_ _3"></span>en<span class="_ _9"> </span>by<span class="_ _11"> </span>the<span class="_ _9"> </span>routine<span class="_ _9"> </span>on</div><div class="t m0 x6 hc y219 ff4 fs7 fc0 sc0 ls0 ws0">each<span class="_ _d"> </span>call.</div><div class="t m0 x6 hc y21a ffd fs7 fc0 sc0 ls0 ws0">Problem:<span class="_ _10"> </span><span class="ff4">The<span class="_ _d"> </span>timer<span class="_ _d"> </span>calls<span class="_ _11"> </span>take<span class="_ _d"> </span>some<span class="_ _d"> </span>time<span class="_ _11"> </span>themselves</span></div><div class="t m0 x10 hc y21b ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff1">Sampling<span class="_"> </span><span class="ff4">The<span class="_"> </span>operating<span class="_"> </span>system<span class="_"> </span>interrupts<span class="_"> </span>the<span class="_"> </span>CPU<span class="_"> </span>at<span class="_"> </span>regula<span class="_ _3"></span>r<span class="_"> </span>intervals<span class="_"> </span>(time<span class="_"> </span>slices)</span></span></div><div class="t m0 x6 hc y21c ff4 fs7 fc0 sc0 ls0 ws0">to<span class="_ _e"> </span>execute<span class="_ _e"> </span>process<span class="_ _e"> </span>switches.<span class="_ _3d"> </span>A<span class="_ _3"></span>t<span class="_ _e"> </span>that<span class="_ _7"> </span>p<span class="_ _6"></span>oint,<span class="_ _7"> </span>a<span class="_ _e"> </span>sampling<span class="_ _e"> </span>profiler<span class="_ _10"> </span>will<span class="_ _e"> </span>record<span class="_ _10"> </span>the</div><div class="t m0 x6 hc y21d ff4 fs7 fc0 sc0 ls0 ws0">currently-executed<span class="_ _d"> </span>instruction</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">59/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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class="t m0 x4 h19 y7 ff6 fs3 fc1 sc0 ls0 ws0">gprof</div><div class="t m0 x1 hc y48 ff6 fs7 fc0 sc0 ls0 ws0">gprof<span class="_ _d"> </span><span class="ff4">is<span class="_ _11"> </span>a<span class="_ _d"> </span>profiling<span class="_ _d"> </span>p<span class="_ _3"></span>rogram<span class="_ _11"> </span>which<span class="_ _d"> </span>collects<span class="_ _11"> </span>and<span class="_ _d"> </span>arranges<span class="_ _d"> </span>timing<span class="_ _d"> </span>statistics<span class="_ _11"> </span>on<span class="_ _11"> </span>a<span class="_ _d"> </span>given</span></div><div class="t m0 x1 hc y72 ff4 fs7 fc0 sc0 ls0 ws0">p<span class="_ _3"></span>rogram.<span class="_ _10"> </span>It<span class="_ _11"> </span>uses<span class="_ _d"> </span>a<span class="_ _11"> </span>hyb<span class="_ _3"></span>rid<span class="_ _11"> </span>of<span class="_ _d"> </span>instrumentation<span class="_ _11"> </span>and<span class="_ _d"> </span>sampling<span class="_ _11"> </span>programs<span class="_ _d"> </span>to<span class="_ _d"> </span>monitor</div><div class="t m0 x1 hc yc4 ffc fs7 fc0 sc0 ls0 ws0">function<span class="_ _d"> </span>calls</div><div class="t m0 x14 hc y20b ff4 fs7 fc0 sc0 ls0 ws0">W<span class="_ _3"></span>ebsite:<span class="_ _10"> </span><span class="ffd fs4">sourceware.org/binutils/docs/gprof/</span></div><div class="t m0 x1 hc y20c ff1 fs7 fc0 sc0 ls0 ws0">Usage:</div><div class="t m0 x10 hc y21e ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">Co<span class="_ _6"></span>de<span class="_ _d"> </span>Instrumentation</span></div><div class="t m0 x51 ha y21f ff10 fs6 fc8 sc0 ls0 ws0">$<span class="_ _a"> </span><span class="ff9 fc0">g++<span class="_ _a"> </span><span class="fc4">-pg<span class="_ _10"> </span></span>[flags]<span class="_ _a"> </span><source_files></span></div><div class="t m0 x6 hd y220 fff fs6 fc0 sc0 ls0 ws0">Imp<span class="_ _6"></span>o<span class="_ _3"></span>rtant:<span class="_ 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class="ff4">R<span class="_ _3"></span>un<span class="_ _11"> </span><span class="ffd">gprof<span class="_ _d"> </span></span>on<span class="_ _11"> </span><span class="ffd">gmon.out</span></span></div><div class="t m0 x51 ha y223 ff10 fs6 fc8 sc0 ls0 ws0">$<span class="_ _a"> </span><span class="ff9 fc4">gprof<span class="_ _a"> </span><span class="fc0"><executable><span class="_ _10"> </span>gmon.out</span></span></div><div class="t m0 x10 hc y224 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">Insp<span class="_ _6"></span>ect<span class="_ _d"> </span><span class="ffd">gprof<span class="_ _d"> </span></span>output</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">60/76</div><a class="l" href="https://sourceware.org/binutils/docs/gprof/"><div class="d m1" style="border-style:none;position:absolute;left:84.888000px;bottom:157.219000px;width:185.056000px;height:11.782000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div 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"/><div class="t m0 x4 h5 y7 ff6 fs3 fc1 sc0 ls0 ws0">gprof<span class="_ _3f"> </span><span class="ff1">2/2</span></div><div class="t m0 x1 hc y225 ffd fs7 fc0 sc0 ls0 ws0">gprof<span class="_ _d"> </span><span class="ff4">output</span></div><div class="t m0 x2d hc y226 ffd fs7 fc0 sc0 ls0 ws0">gprof<span class="_ _d"> </span><span class="ff4">can<span class="_ _11"> </span>b<span class="_ _6"></span>e<span class="_ _d"> </span>also<span class="_ _d"> </span>used<span class="_ _11"> </span>fo<span class="_ _3"></span>r<span class="_ _11"> </span>showing<span class="_ _d"> </span>the<span class="_ _d"> </span>call<span class="_ _d"> </span>graph<span class="_ _11"> </span>statistics</span></div><div class="t m0 x10 ha y227 ff10 fs6 fc8 sc0 ls0 ws0">$<span class="_ _a"> </span><span class="ff9 fc4">gprof<span class="_ _a"> </span><span class="fc0">-q<span class="_ _10"> </span><executable><span class="_ _a"> </span>gmon.out</span></span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">61/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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"/><div class="t m0 x4 h19 y7 ff6 fs3 fc1 sc0 ls0 ws0">uftrace</div><div class="t m0 x1 hc y48 ff4 fs7 fc0 sc0 ls0 ws0">The<span class="_ _d"> </span><span class="ffd">uftrace<span class="_ _11"> </span></span>to<span class="_ _6"></span>ol<span class="_ _d"> </span>is<span class="_ _d"> </span>to<span class="_ _11"> </span>trace<span class="_ _d"> </span>and<span class="_ _11"> </span>analyze<span class="_ _d"> </span>execution<span class="_ _11"> </span>of<span class="_ _d"> </span>a<span class="_ _11"> </span>program<span class="_ _d"> </span>written<span class="_ _d"> </span>in<span class="_ _11"> </span>C/C++</div><div class="t m0 x14 hc y228 ff4 fs7 fc0 sc0 ls0 ws0">W<span class="_ _3"></span>ebsite:<span class="_ _10"> </span><span class="ffd">github.com/namhyung/uftrace</span></div><div class="t m0 x10 he y229 ff1c fs7 fc8 sc0 ls0 ws0">$<span class="_ _16"> </span><span class="ffd fc0">gcc<span class="_ _16"> </span>-pg<span class="_ _16"> </span><program>.cpp</span></div><div class="t m0 x10 he y22a ff1c fs7 fc8 sc0 ls0 ws0">$<span class="_ _16"> </span><span class="ffd fc4">uftrace<span class="_ _16"> </span><span class="fc0">record<span class="_ _16"> </span><executable></span></span></div><div class="t m0 x10 he y22b ff1c fs7 fc8 sc0 ls0 ws0">$<span class="_ _16"> </span><span class="ffd fc4">uftrace<span class="_ _16"> </span><span class="fc0">replay</span></span></div><div class="t m0 x1 hc y22c ff4 fs7 fc0 sc0 ls0 ws0">Flame<span class="_ _d"> </span>graph<span class="_ _11"> </span>output<span class="_ _d"> </span>in<span class="_ _11"> </span><span class="ffd">html<span class="_ _d"> </span></span>and<span class="_ _11"> </span><span class="ffd">svg</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">62/76</div><a class="l" href="https://github.com/namhyung/uftrace"><div class="d m1" style="border-style:none;position:absolute;left:84.888000px;bottom:182.717000px;width:156.629000px;height:11.993000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf45" class="pf w0 h0" data-page-no="45"><div class="pc pc45 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h19 y7 ff6 fs3 fc1 sc0 ls0 ws0">callgrind</div><div class="t m0 x1 hc y22d ff6 fs7 fc0 sc0 ls0 ws0">callgrind<span class="_ _d"> </span><span class="ff4">is<span class="_ _11"> </span>a<span class="_ _d"> </span>profiling<span class="_ _d"> </span>to<span class="_ _6"></span>ol<span class="_ _d"> </span>that<span class="_ _d"> </span>records<span class="_ _d"> </span>the<span class="_ _d"> </span>call<span class="_ _11"> </span>histo<span class="_ _3"></span>ry<span class="_ _11"> </span>among<span class="_ _d"> </span>functions<span class="_ _11"> </span>in<span class="_ _11"> </span>a</span></div><div class="t m0 x1 hc y22e ff4 fs7 fc0 sc0 ls0 ws0">p<span class="_ _3"></span>rogram’s<span class="_ _11"> </span>run<span class="_ _d"> </span>as<span class="_ _11"> </span>a<span class="_ _d"> </span>call-graph.<span class="_ _10"> </span>By<span class="_ _11"> </span>default,<span class="_ _d"> </span>the<span class="_ _11"> </span>collected<span class="_ _d"> </span>data<span class="_ _11"> </span>consists<span class="_ _d"> </span>of<span class="_ _11"> </span>the<span class="_ _11"> </span>numb<span class="_ _6"></span>er<span class="_ _d"> </span>of</div><div class="t m0 x1 hc y22f ff4 fs7 fc0 sc0 ls0 ws0">instructions<span class="_ _d"> </span>executed</div><div class="t m0 x14 hc y230 ff4 fs7 fc0 sc0 ls0 ws0">W<span class="_ _3"></span>ebsite:<span class="_ _10"> </span><span class="ffd fs4">valgrind.org/docs/manual/cl-manual.html</span></div><div class="t m0 x1 hc y231 ff1 fs7 fc0 sc0 ls0 ws0">Usage:</div><div class="t m0 x10 hc y232 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">Profile<span class="_ _d"> </span>the<span class="_ _11"> </span>application<span class="_ _d"> </span>with<span class="_ _11"> </span><span class="ffd">callgrind</span></span></div><div class="t m0 x51 ha y233 ff10 fs6 fc8 sc0 ls0 ws0">$<span class="_ _a"> </span><span class="ff9 fc0">valgrind<span class="_ _a"> </span>--tool<span class="_ _10"> </span><span class="fc4">callgrind<span class="_ _a"> </span></span><executable><span class="_ _10"> </span><args></span></div><div class="t m0 x10 hc y234 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">Insp<span class="_ _6"></span>ect<span class="_ _d"> </span><span class="ffd">callgrind.out.XXX<span class="_ _d"> </span></span>file,<span class="_ _11"> </span>where<span class="_ _d"> </span><span class="ffd">XXX<span class="_ _11"> </span></span>will<span class="_ _d"> </span>b<span class="_ _6"></span>e<span class="_ _d"> </span>the<span class="_ _11"> </span>process<span class="_ _11"> </span>identifier</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">63/76</div><a class="l" href="http://valgrind.org/docs/manual/cl-manual.html"><div class="d m1" style="border-style:none;position:absolute;left:84.888000px;bottom:145.749000px;width:205.977000px;height:11.782000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf46" class="pf w0 h0" data-page-no="46"><div class="pc pc46 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h19 y7 ff6 fs3 fc1 sc0 ls0 ws0">cachegrind</div><div class="t m0 x1 hc y48 ff6 fs7 fc0 sc0 ls0 ws0">cachegrind<span class="_ _d"> </span><span class="ff4">simulates<span class="_ _11"> </span>ho<span class="_ _3"></span>w<span class="_ _11"> </span>your<span class="_ _d"> </span>p<span class="_ _3"></span>rogram<span class="_ _d"> </span>interacts<span class="_ _11"> </span>with<span class="_ _11"> </span>a<span class="_ _d"> </span>machine’s<span class="_ _11"> </span>cache<span class="_ _d"> </span>hierarchy</span></div><div class="t m0 x1 hc y72 ff4 fs7 fc0 sc0 ls0 ws0">and<span class="_ _d"> </span>(optionally)<span class="_ _11"> </span>b<span class="_ _3"></span>ranch<span class="_ _11"> </span>predicto<span class="_ _3"></span>r</div><div class="t m0 x14 hc y177 ff4 fs7 fc0 sc0 ls0 ws0">W<span class="_ _3"></span>ebsite:<span class="_ _10"> </span><span class="ffd fs4">valgrind.org/docs/manual/cg-manual.html</span></div><div class="t m0 x1 hc y235 ff1 fs7 fc0 sc0 ls0 ws0">Usage:</div><div class="t m0 x10 hc y236 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">Profile<span class="_ _d"> </span>the<span class="_ _11"> </span>application<span class="_ _d"> </span>with<span class="_ _11"> </span><span class="ffd">cachegrind</span></span></div><div class="t m0 x51 h1a y237 ff1d fs5 fc8 sc0 ls0 ws0">$<span class="_ _9"> </span><span class="ff1e fc0">valgrind<span class="_ _1d"> </span>--tool<span class="_ _1d"> </span><span class="fc4">cachegrind<span class="_ _9"> </span></span>--branch-sim=yes<span class="_ _1d"> </span><executable><span class="_ _1d"> </span><args></span></div><div class="t m0 x10 hc y238 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ff4">Insp<span class="_ _6"></span>ect<span class="_ _d"> </span>the<span class="_ _d"> </span>output<span class="_ _11"> </span>(cache<span class="_ _d"> </span>misses<span class="_ _11"> </span>and<span class="_ _d"> </span>rate)</span></div><div class="t m0 x11 h6 y239 ff4 fs4 fc0 sc0 ls0 ws0">-<span class="_ _1a"> </span><span class="ffd">l1<span class="_ _1b"> </span></span>L1<span class="_ _c"> </span>instruction<span class="_ _d"> </span>cache</div><div class="t m0 x11 h6 y23a ff4 fs4 fc0 sc0 ls0 ws0">-<span class="_ _1a"> </span><span class="ffd">D1<span class="_ _1b"> </span></span>L1<span class="_ _c"> </span>data<span class="_ _d"> </span>cache</div><div class="t m0 x11 h6 y23b ff4 fs4 fc0 sc0 ls0 ws0">-<span class="_ _1a"> </span><span class="ffd">LL<span class="_ _1b"> </span></span>Last<span class="_ _c"> </span>level<span class="_ _d"> </span>cache</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">64/76</div><a class="l" href="http://valgrind.org/docs/manual/cg-manual.html"><div class="d m1" style="border-style:none;position:absolute;left:84.888000px;bottom:167.346000px;width:205.977000px;height:11.782000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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class="t m0 x4 h5 y7 ff6 fs3 fc1 sc0 ls0 ws0">kcachegrind<span class="_ _1d"> </span><span class="ff1">and<span class="_ _1d"> </span></span>qcachegrindwin<span class="_ _1d"> </span><span class="ff1">(View)</span></div><div class="t m0 x1 hc y23c ff6 fs7 fc0 sc0 ls0 ws0">KCachegrind<span class="_ _d"> </span><span class="ff4">(linux)<span class="_ _11"> </span>and<span class="_ _d"> </span></span>Qcachegrind<span class="_ _11"> </span><span class="ff4">(windo<span class="_ _3"></span>ws)<span class="_ _11"> </span>provide<span class="_ _d"> </span>a<span class="_ _d"> </span>graphical<span class="_ _d"> </span>interface<span class="_ _11"> </span>for</span></div><div class="t m0 x1 hc y23d ff4 fs7 fc0 sc0 ls0 ws0">b<span class="_ _3"></span>rowsing<span class="_ _d"> </span>the<span class="_ _d"> </span>p<span class="_ _6"></span>erfo<span class="_ _3"></span>rmance<span class="_ _11"> </span>results<span class="_ _d"> </span>of<span class="_ _a"> </span><span class="ffd">callgraph</span></div><div class="t m0 x52 hd y23e ffe fs6 fc0 sc0 ls0 ws0">•<span class="ff9">kcachegrind.sourceforge.net/html/Home.html</span></div><div class="t m0 x52 hd y23f ffe fs6 fc0 sc0 ls0 ws0">•<span class="ff9">sourceforge.net/projects/qcachegrindwin</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">65/76</div><a class="l" href="http://kcachegrind.sourceforge.net/html/Home.html"><div class="d m1" style="border-style:none;position:absolute;left:62.496000px;bottom:177.530000px;width:199.701000px;height:10.212000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://sourceforge.net/projects/qcachegrindwin/"><div class="d m1" style="border-style:none;position:absolute;left:62.496000px;bottom:161.949000px;width:185.579000px;height:10.211000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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"/><div class="t m0 x4 h5 y7 ff6 fs3 fc1 sc0 ls0 ws0">gprof2dot<span class="_ _1d"> </span><span class="ff1">(View)</span></div><div class="t m0 x1 hc y48 ff6 fs7 fc0 sc0 ls0 ws0">gprof2dot<span class="_ _d"> </span><span class="ff4">is<span class="_ _11"> </span>a<span class="_ _d"> </span>Python<span class="_ _11"> </span>script<span class="_ _d"> </span>to<span class="_ _11"> </span>convert<span class="_ _d"> </span>the<span class="_ _11"> </span>output<span class="_ _d"> </span>from<span class="_ _11"> </span>many<span class="_ _11"> </span>p<span class="_ _3"></span>rofilers<span class="_ _11"> </span>into<span class="_ _d"> </span>a<span class="_ _11"> </span>dot</span></div><div class="t m0 x1 hc y72 ff4 fs7 fc0 sc0 ls0 ws0">graph</div><div class="t m0 x14 hc y177 ff4 fs7 fc0 sc0 ls0 ws0">W<span class="_ _3"></span>ebsite:<span class="_ _10"> </span><span class="ff9 fs6">github.com/jrfonseca/gprof2dot</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">66/76</div><a class="l" href="https://github.com/jrfonseca/gprof2dot"><div class="d m1" style="border-style:none;position:absolute;left:84.888000px;bottom:167.568000px;width:143.213000px;height:11.560000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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"/><div class="t m0 x4 h5 y7 ff6 fs3 fc1 sc0 ls0 ws0">perf<span class="_ _1d"> </span><span class="ff1">Linux<span class="_ _1d"> </span>profiler<span class="_ _40"> </span>1/2</span></div><div class="t m0 x1 hc y48 ff6 fs7 fc0 sc0 ls0 ws0">Perf<span class="_ _d"> </span><span class="ff4">is<span class="_ _d"> </span>p<span class="_ _6"></span>erformance<span class="_ _c"> </span>monitoring<span class="_ _d"> </span>and<span class="_ _d"> </span>analysis<span class="_ _d"> </span>to<span class="_ _6"></span>ol<span class="_ _d"> </span>for<span class="_ _d"> </span>Linux.<span class="_ _a"> </span>It<span class="_ _11"> </span>uses<span class="_ _d"> </span>statistical<span class="_ _11"> </span>p<span class="_ _3"></span>rofiling,</span></div><div class="t m0 x1 hc y72 ff4 fs7 fc0 sc0 ls0 ws0">where<span class="_ _d"> </span>it<span class="_ _11"> </span>p<span class="_ _6"></span>olls<span class="_ _d"> </span>the<span class="_ _d"> </span>program<span class="_ _d"> </span>and<span class="_ _d"> </span>sees<span class="_ _11"> </span>what<span class="_ _d"> </span>function<span class="_ _11"> </span>is<span class="_ _d"> </span>wo<span class="_ _3"></span>rking</div><div class="t m0 x14 hc y177 ff4 fs7 fc0 sc0 ls0 ws0">W<span class="_ _3"></span>ebsite:<span class="_ _10"> </span><span class="ffd">perf.wiki.kernel.org/index.php/Main<span class="_ _9"> </span>Page</span></div><div class="t m0 x10 ha y240 ff10 fs6 fc8 sc0 ls0 ws0">$<span class="_ _a"> </span><span class="ff9 fc4">perf<span class="_ _a"> </span><span class="fc0">record<span class="_ _10"> </span>-g<span class="_ _a"> </span><executable><span class="_ _10"> </span><args><span class="_ _a"> </span><span class="ff8 fcd">//<span class="_ _a"> </span>or</span></span></span></div><div class="t m0 x10 ha y241 ff10 fs6 fc8 sc0 ls0 ws0">$<span class="_ _a"> </span><span class="ff9 fc4">perf<span class="_ _a"> </span><span class="fc0">record<span class="_ _10"> </span>--call-graph<span class="_ _a"> </span>dwarf<span class="_ _10"> </span><executable></span></span></div><div class="t m0 x10 ha y242 ff10 fs6 fc8 sc0 ls0 ws0">$<span class="_ _a"> </span><span class="ff9 fc4">perf<span class="_ _a"> </span><span class="fc0">report<span class="_ _10"> </span><span class="ff8 fcd">//<span class="_ _a"> </span>or</span></span></span></div><div class="t m0 x10 ha y243 ff10 fs6 fc8 sc0 ls0 ws0">$<span class="_ _a"> </span><span class="ff9 fc4">perf<span class="_ _a"> </span><span class="fc0">report<span class="_ _10"> </span>-g<span class="_ _a"> </span>graph<span class="_ _10"> </span>--no-children</span></span></div><div class="t m0 x10 ha y244 ff9 fs6 fc7 sc0 ls0 ws0">Linux<span class="_ _a"> </span>perf<span class="_ _a"> </span>for<span class="_ _10"> </span>Qt<span class="_ _a"> </span>developers</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">67/76</div><a class="l" href="https://perf.wiki.kernel.org/index.php/Main_Page"><div class="d m1" style="border-style:none;position:absolute;left:84.888000px;bottom:167.136000px;width:229.479000px;height:11.992000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://www.kdab.com/wp-content/uploads/stories/Linux_perf_for_Qt_developers.pdf"><div class="d m1" style="border-style:none;position:absolute;left:34.722000px;bottom:4.193000px;width:133.798000px;height:13.445000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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class="t m0 x4 h5 y7 ff6 fs3 fc1 sc0 ls0 ws0">perf<span class="_ _1d"> </span><span class="ff1">Linux<span class="_ _1d"> </span>profiler<span class="_ _40"> </span>2/2</span></div><div class="t m0 x1 hc y48 ff4 fs7 fc0 sc0 ls0 ws0">Data<span class="_ _d"> </span>collected<span class="_ _11"> </span>b<span class="_ _3"></span>y<span class="_ _11"> </span><span class="ff6">perf<span class="_ _11"> </span></span>can<span class="_ _d"> </span>b<span class="_ _6"></span>e<span class="_ _d"> </span>visualized<span class="_ _11"> </span>by<span class="_ _d"> </span>using<span class="_ _d"> </span>flame<span class="_ _11"> </span>graphs,<span class="_ _d"> </span>see:</div><div class="t m0 x1 he y72 ffd fs7 fc0 sc0 ls0 ws0">Speedscope:<span class="_ _20"> </span>visualize<span class="_ _16"> </span>what<span class="_ _16"> </span>your<span class="_ _16"> </span>program<span class="_ _16"> </span>is<span class="_ _16"> </span>doing<span class="_ _16"> </span>and<span class="_ _16"> </span>where<span class="_ _16"> </span>it<span class="_ _16"> </span>is</div><div class="t m0 x1 he yc4 ffd fs7 fc0 sc0 ls0 ws0">spending<span class="_ _16"> </span>time</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">68/76</div><a class="l" href="https://johnysswlab.com/speedscope-visualize-what-your-program-is-doing-and-where-it-is-spending-time/"><div class="d m1" style="border-style:none;position:absolute;left:27.350000px;bottom:188.172000px;width:398.843000px;height:10.952000px;background-color:rgba(255,255,255,0.000001);"></div></a><a class="l" href="https://johnysswlab.com/speedscope-visualize-what-your-program-is-doing-and-where-it-is-spending-time/"><div class="d m1" style="border-style:none;position:absolute;left:27.350000px;bottom:172.590000px;width:76.447000px;height:10.952000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf4b" class="pf w0 h0" data-page-no="4b"><div class="pc pc4b w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA4wAAAH/CAIAAACTgcSzAAAACXBIWXMAABYlAAAWJQFJUiTwAAAIX0lEQVR42u3YsQ2DMBRFUTvCoswMEVUmiCLqSGzGEhmHgiGyQSrKuDEdM3yFczbw0y+unIfHmAAAIIzPulysAABANCIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAQKQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAIBDrrVaAQCAOEopflIBAAhHpAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAQKQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFJNAACASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAQKQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApJoAAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAQKQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAgEgFAECkAgCASAUA4H9023yzAgBAQN/X+5wPvz+nnK99234ptdZcAgAAIezHJRQEHToSGQAAAABJRU5ErkJggg=="/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Other<span class="_ _1d"> </span>Profilers</div><div class="t m0 x1 hc y245 ff4 fs7 fc0 sc0 ls0 ws0">F<span class="_ _3"></span>ree<span class="_ _11"> </span>profiler:</div><div class="t m0 x10 hc y246 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffd">Hotspot</span></div><div class="t m0 x1 hc y247 ff4 fs7 fc0 sc0 ls0 ws0">Prop<span class="_ _3"></span>rietary<span class="_ _d"> </span>p<span class="_ _3"></span>rofiler:</div><div class="t m0 x10 hc y248 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffd">Intel<span class="_ _16"> </span>VTune</span></div><div class="t m0 x10 hc y249 ffb fs7 fc0 sc0 ls0 ws0">•<span class="_ _7"> </span><span class="ffd">AMD<span class="_ _16"> </span>CodeAnalyst</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">69/76</div><a class="l" href="https://www.kdab.com/hotspot-gui-linux-perf-profiler/"><div class="d m1" style="border-style:none;position:absolute;left:49.168000px;bottom:139.646000px;width:42.084000px;height:10.951000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf4c" class="pf w0 h0" data-page-no="4c"><div class="pc pc4c w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x8 h2 yd4 ff1 fs0 fc0 sc0 ls0 ws0">P<span class="_ _8"></span>a<span class="_ _3"></span>rallel<span class="_ _1"> </span>Computing</div><a class="l" href="#pf4c" data-dest-detail='[76,"XYZ",27.346,256.118,null]'><div class="d m1" style="border-style:none;position:absolute;left:105.775000px;bottom:132.432000px;width:218.157000px;height:24.026000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf4d" class="pf w0 h0" data-page-no="4d"><div class="pc pc4d w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA4wAAAH/CAIAAACTgcSzAAAACXBIWXMAABYlAAAWJQFJUiTwAAAIuElEQVR42u3YMUoEQRBA0WmZwmjwDGI0JxCRDTYQvJmX8C4brIE32MQbGC0dWclsJkYGg0j38N4JqquTT5W7+8cBAACa8fH+dmULAAC0RqQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAPCtZKYtAADQjohwSQUAoDkiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAAEKkAAIhUAAAQqQAAiFQAAPhTY0ezzru9DwMAWOd0PHQ0rUsqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBANi2kpm9zFpr9WEAAOtM09TLqBHhkgoAQHNEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAQKQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAA/6Zkpi0AANCOiHBJBQCgOSIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAADwm7GjWefd3ocBAKxzOh46mtYlFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAABsW8nMXmattfowAIB1pmnqZdSIcEkFAKA5IhUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAAEKkAALSpZKYtAADQjohwSQUAoDkiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAACIVAAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCAIBIBQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgAAIhUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAEQqAAAiFQAARCoAACIVAABEKgAAIhUAAEQqAAAiFQAARCoAAIhUAABEKgAAiFQAAEQqAACIVAAARCoAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAQKQCACBSAQBApAIAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAgEgFAECkAgCASAUAQKQCAIBIBQBApAIAgEgFAACRCgCASAUAAJEKAIBIBQAAkQoAgEgFAACRCgCASAUAAJEKAAAiFQAAkQoAACIVAACRCgAAIhUAAJEKAAAiFQAAkWoFAACIVAAAEKkAAIhUAAAQqQAAiFQAABCpAACIVAAAEKkAACBSAQAQqQAAIFIBABCpAAAgUgEAEKkAACBSAQAQqQAAIFIBAECkAgAgUgEAQKQCACBSAQBApAIAIFIBAECkAgDAT+P55dYWAAB69Pn0usl3zQ/PpdxcL+evYViWxUcDANCEC2M0K/jTI322AAAAAElFTkSuQmCC"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Concurrency<span class="_ _1d"> </span>vs.<span class="_ _16"> </span>Pa<span class="_ _3"></span>rallelism</div><div class="t m0 xb hc y24a ff1 fs7 fc1 sc0 ls0 ws0">Concurrency</div><div class="t m0 xb hc y24b ff4 fs7 fc0 sc0 ls0 ws0">A<span class="_ _d"> </span>system<span class="_ _11"> </span>is<span class="_ _d"> </span>said<span class="_ _11"> </span>to<span class="_ _d"> </span>b<span class="_ _6"></span>e<span class="_ _d"> </span><span class="ff1">concurrent<span class="_ _11"> </span></span>if<span class="_ _d"> </span>it<span class="_ _11"> </span>can<span class="_ _11"> </span>supp<span class="_ _6"></span>o<span class="_ _3"></span>rt<span class="_ _d"> </span>tw<span class="_ _3"></span>o<span class="_ _d"> </span>or<span class="_ _d"> </span>mo<span class="_ _3"></span>re<span class="_ _11"> </span>actions<span class="_ _d"> </span>in<span class="_ _11"> </span>progress</div><div class="t m0 xb hc y24c ff4 fs7 fc0 sc0 ls0 ws0">at<span class="_ _d"> </span>the<span class="_ _11"> </span>same<span class="_ _d"> </span>time.<span class="_ _10"> </span>Multiple<span class="_ _11"> </span>p<span class="_ _3"></span>ro<span class="_ _6"></span>cessing<span class="_ _d"> </span>units<span class="_ _11"> </span>w<span class="_ _3"></span>ork<span class="_ _d"> </span>on<span class="_ _d"> </span>different<span class="_ _11"> </span>tasks<span class="_ _d"> </span>indep<span class="_ _6"></span>endently</div><div class="t m0 xb hc y24d ff1 fs7 fc1 sc0 ls0 ws0">P<span class="_ _3"></span>arallelism</div><div class="t m0 xb hc y24e ff4 fs7 fc0 sc0 ls0 ws0">A<span class="_ _d"> </span>system<span class="_ _11"> </span>is<span class="_ _d"> </span>said<span class="_ _11"> </span>to<span class="_ _d"> </span>b<span class="_ _6"></span>e<span class="_ _d"> </span><span class="ff1">parallel<span class="_ _d"> </span></span>if<span class="_ _d"> </span>it<span class="_ _11"> </span>can<span class="_ _d"> </span>supp<span class="_ _6"></span>o<span class="_ _3"></span>rt<span class="_ _11"> </span>tw<span class="_ _3"></span>o<span class="_ _d"> </span>or<span class="_ _d"> </span>mo<span class="_ _3"></span>re<span class="_ _11"> </span>actions<span class="_ _d"> </span>executing</div><div class="t m0 xb hc y24f ff4 fs7 fc0 sc0 ls0 ws0">simultaneously<span class="_ _8"></span>.<span class="_ _10"> </span>Multiple<span class="_ _d"> </span>processing<span class="_ _11"> </span>units<span class="_ _d"> </span>wo<span class="_ _3"></span>rk<span class="_ _d"> </span>on<span class="_ _11"> </span>the<span class="_ _d"> </span>same<span class="_ _11"> </span>problem<span class="_ _d"> </span>and<span class="_ _d"> </span>their</div><div class="t m0 xb hc y250 ff4 fs7 fc0 sc0 ls0 ws0">interaction<span class="_ _d"> </span>can<span class="_ _11"> </span>effect<span class="_ _d"> </span>the<span class="_ _11"> </span>final<span class="_ _d"> </span>result</div><div class="t m0 x1 hc y251 ff4 fs7 fc0 sc0 ls0 ws0">Note:<span class="_ _10"> </span>pa<span class="_ _3"></span>rallel<span class="_ _11"> </span>computation<span class="_ _d"> </span>requires<span class="_ _11"> </span>rethinking<span class="_ _d"> </span>original<span class="_ _d"> </span>sequential<span class="_ _d"> </span>algorithms<span class="_ _d"> </span>(e.g.</div><div class="t m0 x1 hc y252 ff4 fs7 fc0 sc0 ls0 ws0">avoid<span class="_ _d"> </span>race<span class="_ _11"> </span>conditions)</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">70/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
|
||
<div id="pf4e" class="pf w0 h0" data-page-no="4e"><div class="pc pc4e w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">P<span class="_ _3"></span>erfo<span class="_ _3"></span>rmance<span class="_ _a"> </span>Scaling</div><div class="t m0 xb hc y253 ff1 fs7 fc1 sc0 ls0 ws0">Strong<span class="_ _11"> </span>Scaling</div><div class="t m0 xb hc y254 ff4 fs7 fc0 sc0 ls0 ws0">The<span class="_ _d"> </span><span class="ff1">strong<span class="_ _9"> </span>scaling<span class="_ _11"> </span></span>defined<span class="_ _d"> </span>how<span class="_ _d"> </span>the<span class="_ _d"> </span>compute<span class="_ _11"> </span>time<span class="_ _11"> </span>decreases<span class="_ _d"> </span>increasing<span class="_ _11"> </span>the<span class="_ _d"> </span>numb<span class="_ _6"></span>er</div><div class="t m0 xb hc y255 ff4 fs7 fc0 sc0 ls0 ws0">of<span class="_ _d"> </span>processors<span class="_ _d"> </span>fo<span class="_ _3"></span>r<span class="_ _11"> </span>a<span class="_ _d"> </span>fixed<span class="_ _11"> </span>total<span class="_ _d"> </span>problem<span class="_ _d"> </span>size</div><div class="t m0 xb hc y256 ff1 fs7 fc1 sc0 ls0 ws0">W<span class="_ _3"></span>eak<span class="_ _9"> </span>Scaling</div><div class="t m0 xb hc y257 ff4 fs7 fc0 sc0 ls0 ws0">The<span class="_ _d"> </span><span class="ff1">weak<span class="_ _11"> </span>scaling<span class="_ _11"> </span></span>defined<span class="_ _d"> </span>how<span class="_ _d"> </span>the<span class="_ _d"> </span>compute<span class="_ _11"> </span>time<span class="_ _d"> </span>decrease<span class="_ _11"> </span>increasing<span class="_ _d"> </span>the<span class="_ _11"> </span>numb<span class="_ _6"></span>er<span class="_ _d"> </span>of</div><div class="t m0 xb hc y258 ff4 fs7 fc0 sc0 ls0 ws0">p<span class="_ _3"></span>ro<span class="_ _6"></span>cesso<span class="_ _3"></span>rs<span class="_ _11"> </span>for<span class="_ _d"> </span>a<span class="_ _d"> </span>fixed<span class="_ _d"> </span>total<span class="_ _11"> </span>problem<span class="_ _d"> </span>size<span class="_ _d"> </span>p<span class="_ _6"></span>er<span class="_ _d"> </span>processor</div><div class="t m0 x1 hc y259 ffc fs7 fc0 sc0 ls0 ws0">Strong<span class="_ _d"> </span>scaling<span class="_ _a"> </span><span class="ff4">is<span class="_ _d"> </span>ha<span class="_ _3"></span>rd<span class="_ _d"> </span>to<span class="_ _d"> </span>achieve<span class="_ _d"> </span>b<span class="_ _6"></span>ecause<span class="_ _d"> </span>of<span class="_ _d"> </span>computation<span class="_ _d"> </span>units<span class="_ _d"> </span>communication.<span class="_ _10"> </span><span class="ffc">Strong</span></span></div><div class="t m0 x1 hc y25a ffc fs7 fc0 sc0 ls0 ws0">scaling<span class="_ _a"> </span><span class="ff4">is<span class="_ _11"> </span>in<span class="_ _d"> </span>contrast<span class="_ _11"> </span>to<span class="_ _d"> </span>the<span class="_ _11"> </span>Amdahl’s<span class="_ _d"> </span>Law</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">71/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">Gustafson’s<span class="_ _1d"> </span>La<span class="_ _3"></span>w</div><div class="t m0 xb hc y25b ff1 fs7 fc1 sc0 ls0 ws0">Gustafson’s<span class="_ _11"> </span>Law</div><div class="t m0 xb hc y25c ff4 fs7 fc0 sc0 ls0 ws0">Increasing<span class="_ _d"> </span>numb<span class="_ _6"></span>er<span class="_ _d"> </span>of<span class="_ _11"> </span>p<span class="_ _3"></span>ro<span class="_ _6"></span>cesso<span class="_ _3"></span>r<span class="_ _11"> </span>units<span class="_ _d"> </span>allow<span class="_ _d"> </span>solving<span class="_ _d"> </span>larger<span class="_ _d"> </span>problems<span class="_ _d"> </span>in<span class="_ _d"> </span>the<span class="_ _d"> </span>same<span class="_ _11"> </span>time</div><div class="t m0 xb hc y25d ff4 fs7 fc0 sc0 ls0 ws0">(the<span class="_ _d"> </span>computation<span class="_ _11"> </span>time<span class="_ _d"> </span>is<span class="_ _11"> </span>constant)</div><div class="t m0 x1 hc y25e ff4 fs7 fc0 sc0 ls0 ws0">Multiple<span class="_ _d"> </span>problem<span class="_ _d"> </span>instances<span class="_ _d"> </span>can<span class="_ _11"> </span>run<span class="_ _d"> </span>concurrently<span class="_ _11"> </span>with<span class="_ _d"> </span>more<span class="_ _d"> </span>computational<span class="_ _d"> </span>resources</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">72/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf50" class="pf w0 h0" data-page-no="50"><div class="pc pc50 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">P<span class="_ _3"></span>a<span class="_ _3"></span>rallel<span class="_ _a"> </span>Programming<span class="_ _1d"> </span>Platfo<span class="_ _3"></span>rms<span class="_ _a"> </span>and<span class="_ _1d"> </span>APIs<span class="_ _41"> </span>1/3</div><div class="t m0 x53 hc y25f ff1 fs7 fc0 sc0 ls0 ws0">C++11<span class="_ _11"> </span>Threads<span class="_ _16"> </span><span class="ff4">(+<span class="_ _d"> </span>Pa<span class="_ _3"></span>rallel<span class="_ _d"> </span>STL)<span class="_ _11"> </span>free,<span class="_ _d"> </span>multi-core<span class="_ _d"> </span>CPUs</span></div><div class="t m0 x51 hc y260 ff1 fs7 fc0 sc0 ls0 ws0">Op<span class="_ _6"></span>enMP<span class="_ _e"> </span><span class="ff4">free,<span class="_ _11"> </span>directive-based,<span class="_ _d"> </span>multi-core<span class="_ _d"> </span>CPUs<span class="_ _d"> </span>and<span class="_ _11"> </span>GPUs<span class="_ _d"> </span>(last<span class="_ _11"> </span>versions)</span></div><div class="t m0 x2c hc y261 ff1 fs7 fc0 sc0 ls0 ws0">Op<span class="_ _6"></span>enA<span class="_ _3"></span>CC<span class="_ _7"> </span><span class="ff4">free,<span class="_ _d"> </span>directive-based,<span class="_ _11"> </span>multi-core<span class="_ _d"> </span>CPUs<span class="_ _d"> </span>and<span class="_ _d"> </span>GPUs</span></div><div class="t m0 x54 hc y262 ff1 fs7 fc0 sc0 ls0 ws0">Khronos<span class="_ _11"> </span>Op<span class="_ _6"></span>enCL<span class="_ _7"> </span><span class="ff4">free,<span class="_ _11"> </span>multi-co<span class="_ _3"></span>re<span class="_ _11"> </span>CPUs,<span class="_ _d"> </span>GPUs,<span class="_ _11"> </span>FPGA</span></div><div class="t m0 x5 hc y263 ff1 fs7 fc0 sc0 ls0 ws0">Nvidia<span class="_ _11"> </span>CUDA<span class="_ _e"> </span><span class="ff4">free,<span class="_ _11"> </span>Nvidia<span class="_ _d"> </span>GPUs</span></div><div class="t m0 x12 hc y264 ff1 fs7 fc0 sc0 ls0 ws0">AMD<span class="_ _11"> </span>ROCm<span class="_ _16"> </span><span class="ff4">free,<span class="_ _d"> </span>AMD<span class="_ _11"> </span>GPUs</span></div><div class="t m0 x55 hc y265 ff1 fs7 fc0 sc0 ls0 ws0">HIP<span class="_ _7"> </span><span class="ff4">free,<span class="_ _d"> </span>heterogeneous-compute<span class="_ _11"> </span>Interface<span class="_ _d"> </span>for<span class="_ _d"> </span>AMD/Nvidia<span class="_ _d"> </span>GPUs</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">73/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf51" class="pf w0 h0" data-page-no="51"><div class="pc pc51 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="data:image/png;base64,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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">P<span class="_ _3"></span>a<span class="_ _3"></span>rallel<span class="_ _a"> </span>Programming<span class="_ _1d"> </span>Platfo<span class="_ _3"></span>rms<span class="_ _a"> </span>and<span class="_ _1d"> </span>APIs<span class="_ _41"> </span>2/3</div><div class="t m0 xb hc y25f ff1 fs7 fc0 sc0 ls0 ws0">Khronos<span class="_ _11"> </span>SyCL<span class="_ _16"> </span><span class="ff4">free,<span class="_ _d"> </span>abstraction<span class="_ _11"> </span>la<span class="_ _3"></span>yer<span class="_ _d"> </span>fo<span class="_ _3"></span>r<span class="_ _11"> </span>Op<span class="_ _6"></span>enCL,<span class="_ _d"> </span>Op<span class="_ _6"></span>enMP<span class="_ _8"></span>,<span class="_ _d"> </span>C/C++<span class="_ _11"> </span>lib<span class="_ _3"></span>raries,</span></div><div class="t m0 x31 hc y266 ff4 fs7 fc0 sc0 ls0 ws0">multi-co<span class="_ _3"></span>re<span class="_ _11"> </span>CPUs<span class="_ _d"> </span>and<span class="_ _11"> </span>GPUs</div><div class="t m0 x20 hc y267 ff1 fs7 fc0 sc0 ls0 ws0">K<span class="_ _3"></span>oKKos<span class="_ _11"> </span>(Sandia)<span class="_ _7"> </span><span class="ff4">free,<span class="_ _d"> </span>abstraction<span class="_ _11"> </span>lay<span class="_ _3"></span>er<span class="_ _d"> </span>for<span class="_ _d"> </span>multi-co<span class="_ _3"></span>re<span class="_ _11"> </span>CPUs<span class="_ _d"> </span>and<span class="_ _11"> </span>GPUs</span></div><div class="t m0 x56 hc y268 ff1 fs7 fc0 sc0 ls0 ws0">Raja<span class="_ _11"> </span>(LLNL)<span class="_ _16"> </span><span class="ff4">free,<span class="_ _d"> </span>abstraction<span class="_ _11"> </span>la<span class="_ _3"></span>yer<span class="_ _d"> </span>fo<span class="_ _3"></span>r<span class="_ _11"> </span>multi-core<span class="_ _c"> </span>CPUs<span class="_ _11"> </span>and<span class="_ _11"> </span>GPUs</span></div><div class="t m0 x57 hc y269 ff1 fs7 fc0 sc0 ls0 ws0">Intel<span class="_ _11"> </span>TBB<span class="_ _16"> </span><span class="ff4">commercial,<span class="_ _d"> </span>multi-core<span class="_ _d"> </span>CPUs</span></div><div class="t m0 x11 hc y26a ff1 fs7 fc0 sc0 ls0 ws0">OneAPI<span class="_ _7"> </span><span class="ff4">free,<span class="_ _d"> </span>Data<span class="_ _11"> </span>Pa<span class="_ _3"></span>rallel<span class="_ _d"> </span>C++<span class="_ _d"> </span>(DPC++)<span class="_ _11"> </span>built<span class="_ _d"> </span>up<span class="_ _6"></span>on<span class="_ _d"> </span>C++<span class="_ _11"> </span>and<span class="_ _11"> </span>SYCL,</span></div><div class="t m0 x31 hc y26b ff4 fs7 fc0 sc0 ls0 ws0">CPUs,<span class="_ _d"> </span>GPUs,<span class="_ _11"> </span>FPGA,<span class="_ _d"> </span>accelerators</div><div class="t m0 x58 hc y26c ff1 fs7 fc0 sc0 ls0 ws0">MPI<span class="_ _7"> </span><span class="ff4">free,<span class="_ _d"> </span>de-facto<span class="_ _11"> </span>standard<span class="_ _c"> </span>for<span class="_ _d"> </span>distributed<span class="_ _d"> </span>system</span></div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">74/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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<div id="pf52" class="pf w0 h0" data-page-no="52"><div class="pc pc52 w0 h0"><img class="bi x0 y0 w1 h1" alt="" 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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">P<span class="_ _3"></span>a<span class="_ _3"></span>rallel<span class="_ _a"> </span>Programming<span class="_ _1d"> </span>Platfo<span class="_ _3"></span>rms<span class="_ _a"> </span>and<span class="_ _1d"> </span>APIs<span class="_ _41"> </span>3/3</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">75/76</div></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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"/><div class="t m0 x4 h5 y7 ff1 fs3 fc1 sc0 ls0 ws0">A<span class="_ _1d"> </span>Nice<span class="_ _1d"> </span>Example</div><div class="t m0 x1 hc y48 ff4 fs7 fc0 sc0 ls0 ws0">A<span class="_ _3"></span>ccelerates<span class="_ _11"> </span>computational<span class="_ _d"> </span>chemistry<span class="_ _11"> </span>simulations<span class="_ _d"> </span>from<span class="_ _11"> </span>14<span class="_ _d"> </span>hours<span class="_ _11"> </span>to<span class="_ _11"> </span>47<span class="_ _d"> </span>seconds<span class="_ _11"> </span>with</div><div class="t m0 x1 hc y72 ff4 fs7 fc0 sc0 ls0 ws0">Op<span class="_ _6"></span>enA<span class="_ _3"></span>CC<span class="_ _d"> </span>on<span class="_ _11"> </span>GPUs<span class="_ _d"> </span>(<span class="ffa">∼<span class="_ _c"> </span></span>1<span class="ff1b">,<span class="_ _36"> </span></span>000<span class="ffc">x<span class="_ _10"> </span></span>Speedup)</div><div class="t m0 x10 ha y26d ff9 fs6 fc7 sc0 ls0 ws0">Accelerating<span class="_ _a"> </span>Prediction<span class="_ _a"> </span>of<span class="_ _10"> </span>Chemical<span class="_ _a"> </span>Shift<span class="_ _10"> </span>of<span class="_ _a"> </span>Protein<span class="_ _a"> </span>Structures<span class="_ _10"> </span>on<span class="_ _a"> </span>GPUs</div><div class="t m0 x13 h8 y11 ff5 fs5 fc0 sc0 ls0 ws0">76/76</div><a class="l" href="https://www.biorxiv.org/content/10.1101/2020.01.12.903468v1"><div class="d m1" style="border-style:none;position:absolute;left:34.722000px;bottom:7.558000px;width:336.214000px;height:13.445000px;background-color:rgba(255,255,255,0.000001);"></div></a></div><div class="pi" data-data='{"ctm":[1.000000,0.000000,0.000000,1.000000,0.000000,0.000000]}'></div></div>
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