Add exercise2b
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Exercise2b/README.md
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Exercise2b/README.md
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# Exercise 2b
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Execute numpy code with multiple threads.
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```NOTE``` Remember to use `htop` in your terminal to track what the CPUs are doing.
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## First
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Use the script `heavy_computation.py` with different numbers of threads.
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`OMP_NUM_THREADS` can be used to override the number of threads used:
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```
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OMP_NUM_THREADS=7 python heavy_computation.py
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```
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The script will save the timing results into a `timings/` folder as `.txt` files.
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> What happens if `OMP_NUM_THREADS` is not set? How many threads are there? Why?
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## Second
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Plot the timing results from the first part, we wrote the IO for you in `timing_plot.py`.
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1. Plot a graph of execution duration vs. the number of threads
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2. Plot the execution speedup with respect to running a single-threaded process
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Open a PR with your plotting code and post your plots in the conversation, don't upload binaries to the Git remote!
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> What does the result tell us about the optimum number of threads? Why?
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> Does it take the same time as your colleagues to run? Why?
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## Extra
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Investigate the runtime variability. Systematically run multiple instances with the same number of threads by modifying `heavy_computation.py`.
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### Extra extra
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How is the runtime affected when the problem becomes bigger? Is the optimum number of threads always the same?
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How is the runtime affected when the memory is almost full? You can fill it up by creating a separate (unused) large numpy array.
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How about running on battery vs having your laptop plugged in?
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Exercise2b/heavy_computation.py
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Exercise2b/heavy_computation.py
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import os
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import timeit
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import numpy as np
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from datetime import datetime
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import time
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# Timestamp that will be put in the file name
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timestamp = datetime.now().strftime("%H%M%S%f")
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# Get the environment variable for threads
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threads = os.getenv('OMP_NUM_THREADS')
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# A relatively large matrix to work on
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n = 5_000
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x = np.random.random(size=(n, n))
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print(f"We are executed with OMP_NUM_THREADS={threads} for {n=}")
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# Measure the time required for matrix multiplication
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start_time = time.time()
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y = x @ x # The heavy compute
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stop_time = time.time()
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elapsed_time = stop_time - start_time
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print(f'Time used for matrix multiplication: {elapsed_time:.2f} s')
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# Check if timings folder exists
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if not os.path.isdir('timings/'): os.mkdir('timings')
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# IO: Save the timing to a unique txt file
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with open(f'timings/{threads}_threads_t{timestamp}.txt', 'w') as file:
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file.write(f'{threads},{elapsed_time:.6f}')
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Exercise2b/plot.py
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Exercise2b/plot.py
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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# IO: This loads the timings for you
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threads, timings = [], []
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for file in os.listdir('timings'):
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with open(f'timings/{file}', 'r') as f:
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n, t = f.read().strip().split(',')
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threads.append(int(n))
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timings.append(float(t))
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threads = np.array(threads)
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timings = np.array(timings)
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print('This is the data I loaded: threads =', threads, ', timings =',timings)
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fig, axs = plt.subplots()
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# CREATE YOUR PLOT HERE
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# Remember to label your axis
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# Feel free to make it pretty
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plt.savefig('threads_v_timings.png', dpi=300)
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