ch20: minor edits

20-concurrency/primes/primes.py

@@ -1,3 +1,5 @@
+#!/usr/bin/env python3
+
 import math
 
 
@@ -27,7 +29,7 @@ PRIME_FIXTURE = [
 NUMBERS = [n for n, _ in PRIME_FIXTURE]
 
 # tag::IS_PRIME[]
-def is_prime(n) -> bool:
+def is_prime(n: int) -> bool:
     if n < 2:
         return False
     if n == 2:
@@ -35,7 +37,7 @@ def is_prime(n) -> bool:
     if n % 2 == 0:
         return False
 
-    root = math.floor(math.sqrt(n))
+    root = math.isqrt(n)
     for i in range(3, root + 1, 2):
         if n % i == 0:
             return False

20-concurrency/primes/procs.py

@@ -1,28 +1,35 @@
+#!/usr/bin/env python3
+
+"""
+procs.py: shows that multiprocessing on a multicore machine
+can be faster than sequential code for CPU-intensive work.
+"""
+
 # tag::PRIMES_PROC_TOP[]
 from time import perf_counter
-from typing import Tuple, NamedTuple
+from typing import NamedTuple
 from multiprocessing import Process, SimpleQueue, cpu_count  # <1>
 from multiprocessing import queues  # <2>
 import sys
 
 from primes import is_prime, NUMBERS
 
-class Result(NamedTuple):  # <3>
+class PrimeResult(NamedTuple):  # <3>
-    flag: bool
+    n: int
+    prime: bool
     elapsed: float
 
 JobQueue = queues.SimpleQueue[int]  # <4>
-ResultQueue = queues.SimpleQueue[Tuple[int, Result]]  # <5>
+ResultQueue = queues.SimpleQueue[PrimeResult]  # <5>
 
-def check(n: int) -> Result:  # <6>
+def check(n: int) -> PrimeResult:  # <6>
     t0 = perf_counter()
     res = is_prime(n)
-    return Result(res, perf_counter() - t0)
+    return PrimeResult(n, res, perf_counter() - t0)
 
 def worker(jobs: JobQueue, results: ResultQueue) -> None:  # <7>
     while n := jobs.get():  # <8>
-        result = check(n)  # <9>
+        results.put(check(n))  # <9>
-        results.put((n, result))  # <10>
 # end::PRIMES_PROC_TOP[]
 
 # tag::PRIMES_PROC_MAIN[]
@@ -32,11 +39,11 @@ def main() -> None:
     else:
         workers = int(sys.argv[1])
 
-    t0 = perf_counter()
+    print(f'Checking {len(NUMBERS)} numbers with {workers} processes:')
+
     jobs: JobQueue = SimpleQueue() # <2>
     results: ResultQueue = SimpleQueue()
-
+    t0 = perf_counter()
-    print(f'Checking {len(NUMBERS)} numbers with {workers} processes:')
 
     for n in NUMBERS:  # <3>
         jobs.put(n)
@@ -47,7 +54,7 @@ def main() -> None:
         jobs.put(0)  # <6>
 
     while True:
-        n, (prime, elapsed) = results.get()  # <7>
+        n, prime, elapsed = results.get()  # <7>
         label = 'P' if prime else ' '
         print(f'{n:16}  {label} {elapsed:9.6f}s')  # <8>
         if jobs.empty():  # <9>

20-concurrency/primes/sequential.py

@@ -1,18 +1,26 @@
+#!/usr/bin/env python3
+
+"""
+sequential.py: baseline for comparing sequential, multiprocessing,
+and threading code for CPU-intensive work.
+"""
+
 from time import perf_counter
 from typing import NamedTuple
 
 from primes import is_prime, NUMBERS
 
 class Result(NamedTuple):  # <1>
-    flag: bool
+    prime: bool
     elapsed: float
 
 def check(n: int) -> Result:  # <2>
     t0 = perf_counter()
-    flag = is_prime(n)
+    prime = is_prime(n)
-    return Result(flag, perf_counter() - t0)
+    return Result(prime, perf_counter() - t0)
 
 def main() -> None:
+    print(f'Checking {len(NUMBERS)} numbers sequentially:')
     t0 = perf_counter()
     for n in NUMBERS:  # <3>
         prime, elapsed = check(n)

20-concurrency/primes/spinner_prime_async_nap.py

@@ -1,4 +1,4 @@
-# spinner_async_experiment.py
+# spinner_prime_async_nap.py
 
 # credits: Example by Luciano Ramalho inspired by
 # Michele Simionato's multiprocessing example in the python-list:
@@ -8,7 +8,7 @@ import asyncio
 import itertools
 import math
 
-# tag::SPINNER_ASYNC_NAP[]
+# tag::PRIME_NAP[]
 async def is_prime(n):
     if n < 2:
         return False
@@ -17,15 +17,14 @@ async def is_prime(n):
     if n % 2 == 0:
         return False
 
-    sleep = asyncio.sleep  # <1>
+    root = math.isqrt(n)
-    root = math.floor(math.sqrt(n))
     for i in range(3, root + 1, 2):
         if n % i == 0:
             return False
         if i % 100_000 == 1:  # <2>
-            await sleep(0)
+            await asyncio.sleep(0)
     return True
-# end::SPINNER_ASYNC_NAP[]
+# end::PRIME_NAP[]
 
 
 async def spin(msg: str) -> None:

20-concurrency/primes/threads.py

@@ -1,5 +1,12 @@
+#!/usr/bin/env python3
+
+"""
+threads.py: shows that Python threads are slower than
+sequential code for CPU-intensive work.
+"""
+
 from time import perf_counter
-from typing import Tuple, NamedTuple
+from typing import NamedTuple
 from threading import Thread
 from queue import SimpleQueue
 import sys
@@ -7,22 +14,22 @@ import os
 
 from primes import is_prime, NUMBERS
 
-class Result(NamedTuple):
+class PrimeResult(NamedTuple):  # <3>
-    flag: bool
+    n: int
+    prime: bool
     elapsed: float
 
 JobQueue = SimpleQueue[int]
-ResultQueue = SimpleQueue[Tuple[int, Result]]
+ResultQueue = SimpleQueue[PrimeResult]
 
-def check(n: int) -> Result:
+def check(n: int) -> PrimeResult:
     t0 = perf_counter()
     res = is_prime(n)
-    return Result(res, perf_counter() - t0)
+    return PrimeResult(n, res, perf_counter() - t0)
 
 def worker(jobs: JobQueue, results: ResultQueue) -> None:
     while n := jobs.get():
-        result = check(n)
+        results.put(check(n))
-        results.put((n, result))
 
 def main() -> None:
     if len(sys.argv) < 2:  # <1>
@@ -30,11 +37,11 @@ def main() -> None:
     else:
         workers = int(sys.argv[1])
 
-    t0 = perf_counter()
+    print(f'Checking {len(NUMBERS)} numbers with {workers} threads:')
+
     jobs: JobQueue = SimpleQueue() # <2>
     results: ResultQueue = SimpleQueue()
-
+    t0 = perf_counter()
-    print(f'Checking {len(NUMBERS)} numbers with {workers} threads:')
 
     for n in NUMBERS:  # <3>
         jobs.put(n)
@@ -45,7 +52,7 @@ def main() -> None:
         jobs.put(0)  # <6>
 
     while True:
-        n, (prime, elapsed) = results.get()  # <7>
+        n, prime, elapsed = results.get()  # <7>
         label = 'P' if prime else ' '
         print(f'{n:16}  {label} {elapsed:9.6f}s')
         if jobs.empty():  # <8>