renaming exercises

exercises/exerciseA/README.md

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+# Exercise A: multithreading with NumPy
+
+Objective: investigate speed-up of numpy code with multiple threads.
+
+```HINT``` Use `htop` in your terminal to track what the CPUs are doing.
+
+## First
+
+The script `heavy_computation.py` performs some matrix calculations with numpy.
+
+You can change the number of threads that numpy uses for the calculation
+using the `OMP_NUM_THREADS` environment variable like this:
+```
+OMP_NUM_THREADS=7 python heavy_computation.py
+```
+
+The script will also measure the time to run the calculation and will save
+the timing results into the `timings/` folder as a `.txt` file.
+
+**TASK**: Execute the script `heavy_computation.py`, varying the numbers of threads.
+You will plot the resulting calculating times in the second part below.
+
+**QUESTION**
+> What happens if `OMP_NUM_THREADS` is not set? How many threads are there? Why?
+
+
+## Second
+
+In `plot.py`, we have given code that will load all of the timing data in `timings/`.
+
+**TASK**: Add code to plot of the execution duration vs. the number of threads
+
+Open a PR with your plotting code and post your plots in the conversation, don't upload binaries to the Git remote!
+
+**OPTIONAL TASK**: Add code to calculate and plot the speed-up time compared
+to single-threaded execution. Include your code and plot in the PR.
+
+**QUESTIONS**
+
+> What does the result tell us about the optimum number of threads? Why?
+
+> Does it take the same time as your colleagues to run? Why?
+
+## Optional tasks
+
+Investigate the runtime variability. Systematically run multiple instances with the same number of threads by modifying `heavy_computation.py`.
+
+How is the runtime affected when the problem becomes bigger? Is the optimum number of threads always the same?
+
+How is the runtime affected when the memory is almost full? You can fill it up by creating a separate (unused) large numpy array.
+
+How about running on battery vs. having your laptop plugged in?

exercises/exerciseA/heavy_computation.py

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+import os
+import timeit
+import numpy as np
+from datetime import datetime
+import time
+
+# Timestamp that will be put in the file name
+timestamp = datetime.now().strftime("%H%M%S%f")
+
+# Get the environment variable for threads
+threads = os.getenv('OMP_NUM_THREADS')
+
+# A relatively large matrix to work on
+n = 5_000
+x = np.random.random(size=(n, n))
+
+print(f"We are executed with OMP_NUM_THREADS={threads} for {n=}")
+
+# Measure the time required for matrix multiplication
+start_time = time.time()
+y = x @ x  # The heavy compute
+stop_time = time.time()
+elapsed_time = stop_time - start_time
+
+print(f'Time used for matrix multiplication: {elapsed_time:.2f} s')
+
+# Check if timings folder exists
+if not os.path.isdir('timings/'): os.mkdir('timings')
+
+# IO: Save the timing to a unique txt file
+with open(f'timings/{threads}_threads_t{timestamp}.txt', 'w') as file:
+    file.write(f'{threads},{elapsed_time:.6f}')

exercises/exerciseA/plot.py

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+import os
+import numpy as np
+import matplotlib.pyplot as plt
+
+# IO: This loads the timings for you 
+threads, timings = [], []
+for file in os.listdir('timings'):
+    with open(f'timings/{file}', 'r') as f:
+        n, t = f.read().strip().split(',')
+        threads.append(int(n))
+        timings.append(float(t))
+threads = np.array(threads)
+timings = np.array(timings)
+
+print('This is the data I loaded: threads =', threads, ', timings =',timings)
+
+fig, axs = plt.subplots()
+
+# CREATE YOUR PLOT HERE
+# Remember to label your axis
+# Feel free to make it pretty
+
+plt.savefig('threads_v_timings.png', dpi=300)