test different exercise layout - chap 14
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Bogumił Kamiński
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@ -14,72 +14,9 @@ average number of items from this set that were drawn at least once.
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Call the function running this simulation `boot`.
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### Exercise 2
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Now write a function `simboot` that takes parameters `n` and `k` and runs
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the simulation defined in the `boot` function `k` times. It should return
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a named tuple storing `k`, `n`, mean of produced values, and ends of
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approximated 95% confidence interval of the result.
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Make this function single threaded. Check how long
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this function runs for `n=1000` and `k=1_000_000`.
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### Exercise 3
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Now rewrite this simulator to be multi threaded. Use 4 cores for benchmarking.
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Call the function `simbootT`. Check how long this function runs for `n=1000` and
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`k=1_000_000`.
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### Exercise 4
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Now rewrite `boot` and `simbootT` to perform less allocations. Achieve this by
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making sure that all allocated objects are passed to `boot` function (so that it
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does not do any allocations internally). Call these new functions `boot!` and
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`simbootT2`. You might need to use the `Threads.threadid` and `Threads.nthreads`
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functions.
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### Exercise 5
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Use either of the solutions we have developed in the previous exercises to
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create a web service taking `k` and `n` parameters and returning the values
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produced by `boot` functions and time to run the simulation. You might want to
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use the `@timed` macro in your solution.
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Start the server.
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### Exercise 6
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Query the server started in the exercise 5 with
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the following parameters:
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* `k=1000` and `n=1000`
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* `k=1.5` and `n=1000`
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### Exercise 7
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Collect the data generated by a web service into the `df` data frame for
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`k = [10^i for i in 3:6]` and `n = [10^i for i in 1:3]`.
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### Exercise 8
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Replace the `value` column in the `df` data frame by its contents in-place.
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### Exercise 9
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Checks that execution time roughly scales proportionally to the product
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of `k` times `n`.
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### Exercise 10
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Plot the expected fraction of seen elements in the set as a function of
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`n` by `k` along with 95% confidence interval around these values.
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# Solutions
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<details>
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<summary>Show!</summary>
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### Exercise 1
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<summary>Solution</summary>
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Solution (there are many other approaches you could use):
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@ -95,8 +32,22 @@ function boot(n::Integer)
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end
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```
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</details>
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### Exercise 2
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Now write a function `simboot` that takes parameters `n` and `k` and runs
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the simulation defined in the `boot` function `k` times. It should return
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a named tuple storing `k`, `n`, mean of produced values, and ends of
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approximated 95% confidence interval of the result.
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Make this function single threaded. Check how long
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this function runs for `n=1000` and `k=1_000_000`.
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<details>
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<summary>Solution</summary>
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Solution:
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```
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@ -123,8 +74,18 @@ julia> @time simboot(1000, 1_000_000)
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We see that on my computer the run time is around 7 seconds.
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</details>
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### Exercise 3
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Now rewrite this simulator to be multi threaded. Use 4 cores for benchmarking.
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Call the function `simbootT`. Check how long this function runs for `n=1000` and
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`k=1_000_000`.
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<details>
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<summary>Solution</summary>
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Solution:
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```
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@ -153,8 +114,20 @@ julia> @time simbootT(1000, 1_000_000)
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Indeed we see a significant performance improvement.
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</details>
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### Exercise 4
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Now rewrite `boot` and `simbootT` to perform less allocations. Achieve this by
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making sure that all allocated objects are passed to `boot` function (so that it
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does not do any allocations internally). Call these new functions `boot!` and
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`simbootT2`. You might need to use the `Threads.threadid` and `Threads.nthreads`
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functions.
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<details>
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<summary>Solution</summary>
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Solution:
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```
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@ -195,8 +168,21 @@ Indeed, we see that the number of allocations was decreased, which should lower
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GC usage. However, the runtime of the simulation is similar since in this task
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memory allocation does not account for a significant portion of the runtime.
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</details>
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### Exercise 5
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Use either of the solutions we have developed in the previous exercises to
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create a web service taking `k` and `n` parameters and returning the values
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produced by `boot` functions and time to run the simulation. You might want to
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use the `@timed` macro in your solution.
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Start the server.
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<details>
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<summary>Solution</summary>
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Solution (I used the simplest single-threaded code here; this is a complete
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code of the web service):
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@ -238,8 +224,19 @@ end
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Genie.Server.up()
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```
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</details>
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### Exercise 6
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Query the server started in the exercise 5 with
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the following parameters:
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* `k=1000` and `n=1000`
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* `k=1.5` and `n=1000`
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<details>
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<summary>Solution</summary>
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Solution:
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```
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@ -274,8 +271,17 @@ Transfer-Encoding: chunked
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As expected we got a positive answer the first time and an error on the second call.
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</details>
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### Exercise 7
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Collect the data generated by a web service into the `df` data frame for
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`k = [10^i for i in 3:6]` and `n = [10^i for i in 1:3]`.
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<details>
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<summary>Solution</summary>
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Solution:
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```
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@ -316,8 +322,16 @@ julia> df
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12 │ OK 7.23958 {\n "k": 1000000,\n "n…
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```
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</details>
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### Exercise 8
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Replace the `value` column in the `df` data frame by its contents in-place.
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<details>
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<summary>Solution</summary>
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Solution:
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```
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@ -340,8 +354,17 @@ julia> select!(df, :status, :time, :value => AsTable)
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12 │ OK 7.23958 1000000 1000 0.63232 0.632301 0.63234
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```
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</details>
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### Exercise 9
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Checks that execution time roughly scales proportionally to the product
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of `k` times `n`.
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<details>
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<summary>Solution</summary>
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Solution:
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```
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@ -366,8 +389,17 @@ single sample stabilizes (for small values the runtime is low so the timing is
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more affected by external noise and the other operations that the functions do
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affect the results more).
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</details>
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### Exercise 10
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Plot the expected fraction of seen elements in the set as a function of
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`n` by `k` along with 95% confidence interval around these values.
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<details>
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<summary>Solution</summary>
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Solution:
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```
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using Plots
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