add updated exercise

exercises/exerciseC/plot.py

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+import sys
+import collections
+import numpy as np
+
+dts = collections.defaultdict(dict)
+
+for fname in sys.argv[1:]:
+    values = open(fname).read().split()
+    n1 = int(values[0])
+    n2 = int(values[1])
+    dt = float(values[2])
+
+    dts[n1][n2] = dt
+
+print(dts)
+N1 = max(dts)
+N2 = max(max(v) for v in dts.values())
+
+print(N1, N2)
+
+x = np.empty((N1 + 1, N2 + 1))
+for n1, values in dts.items():
+    for n2, v in values.items():
+        x[n1, n2] = v
+
+x[:, 0] = np.nan
+x[0, :] = np.nan
+
+print(x)
+
+from matplotlib import pyplot
+
+fig, axes = pyplot.subplots()
+im = axes.imshow(x, origin='lower')
+axes.set_ylabel('# processes')
+axes.set_xlabel('# threads')
+axes.spines[['right', 'top']].set_visible(False)
+axes.set_title('time')
+fig.colorbar(im, ax=axes, label='s')
+
+fig_small, axes = pyplot.subplots()
+im = axes.imshow(x[:5, :5], origin='lower')
+axes.set_ylabel('# processes')
+axes.set_xlabel('# threads')
+axes.spines[['right', 'top']].set_visible(False)
+axes.set_title('time')
+fig_small.colorbar(im, ax=axes, label='s')
+
+workers = np.arange(N1 + 1)[:, None] * np.arange(N2 + 1)
+speedup = x[1,1] / x
+
+speedup[:, 0] = np.nan
+speedup[0, :] = np.nan
+
+figs, axes = pyplot.subplots()
+im = axes.imshow(speedup, origin='lower')
+axes.set_ylabel('# processes')
+axes.set_xlabel('# threads')
+axes.spines[['right', 'top']].set_visible(False)
+axes.set_title('speedup')
+figs.colorbar(im, ax=axes, label='s')
+
+fig.savefig('time.svg')
+fig_small.savefig('time_inset.svg')
+figs.savefig('speedup.svg')
+
+pyplot.show()

exercises/exerciseC/process_images.py

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+import os
+import sys
+from multiprocessing import Pool as ProcessPool
+import time
+
+def process_image(fname):
+    n_threads = os.getenv('OMP_NUM_THREADS', '(unset)')
+    print(f"Worker {fname=} OMP_NUM_THREADS={n_threads}")
+
+    # An image is an array with width, height and three (RGB) color channels
+    # (Sometimes there is a transparency channel too: RGBA)
+    im = Image.open(fname)
+    try:
+        A = np.median(im, axis=2)[::4, ::4]
+    except:
+        A = np.array(im)[::4, ::4]
+
+    # Decompose image
+    U, S, Vh = np.linalg.svd(A)
+
+    # Remove first singular value
+    S[0] = 0
+    smat = np.zeros(A.shape, dtype=complex)
+    smat[:min(A.shape), :min(A.shape)] = np.diag(S)
+
+    # Re-compose image
+    A = np.dot(U, np.dot(smat, Vh)).real
+    A = (256*(A - A.min())/A.max()).astype('uint8')
+
+    return A
+    
+
+if __name__ == '__main__':
+    n_processes = int(sys.argv[1])
+    n_threads = int(sys.argv[2])
+    fnames = sys.argv[3:]
+
+    # Check that the output folders exist, or create them if needed
+    if not os.path.isdir('processed_images'): os.mkdir('processed_images')
+    if not os.path.isdir('timings'): os.mkdir('timings')
+
+    print(f"Controller with {n_processes} processes and {n_threads} threads / worker")
+
+    # The environment that is set in the parent is inherited by child workers,
+    # we need to set the variable before numpy is imported!
+    os.environ['OMP_NUM_THREADS'] = str(n_threads)
+
+    # We delay the import of numpy because we want to set OMP_NUM_THREADS.
+    # We delay the import of PIL in case it uses numpy internally.
+    import numpy as np
+    from PIL import Image
+
+    # Time the execution
+    start_time = time.time()
+    with ProcessPool(n_processes) as p:
+        new_images = p.map(process_image, fnames)
+    elapsed_time = time.time() - start_time
+
+    for im, fname in zip(new_images, fnames):
+        im = Image.fromarray(im)
+        im.save(fname.replace('images', 'processed_images'))
+
+    print(f'{n_processes} processes and {n_threads} threads and {len(fnames)} jobs: {elapsed_time}')
+
+    # IO: Save the timing to a unique txt file
+    filename = f'timings/{n_processes:02}_processes_{n_threads:02}_threads.txt'
+    with open(filename, 'w') as file:
+        file.write(f'{n_processes} {n_threads} {elapsed_time:.6f}')
+        

exercises/exerciseC/run_with_all_configurations.sh

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+# This is bash, it executes the python script multiple times
+for i in {1..10}
+do
+    for j in {1..10}
+    do
+        python process_images.py $i $j images/*
+    done
+done