exercises/exerciseA/heavy_computation.py

@@ -5,28 +5,29 @@ 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')
+for threads in range(1,12):
+	for _ in range(3):
+		timestamp = datetime.now().strftime("%H%M%S%f")
 
-# A relatively large matrix to work on
+		# A relatively large matrix to work on
-n = 5_000
+		n = 5_000
-x = np.random.random(size=(n, n))
+		x = np.random.random(size=(n, n))
 
-print(f"We are executed with OMP_NUM_THREADS={threads} for {n=}")
+		print(f"We are executed with OMP_NUM_THREADS={threads} for {n=}")
 
-# Measure the time required for matrix multiplication
+		# Measure the time required for matrix multiplication
-start_time = time.time()
+		start_time = time.time()
-y = x @ x  # The heavy compute
+		y = x @ x  # The heavy compute
-elapsed_time = time.time() - start_time
+		elapsed_time = time.time() - start_time
 
-print(f'Time used for matrix multiplication: {elapsed_time:.2f} s')
+		print(f'Time used for matrix multiplication: {elapsed_time:.2f} s')
 
-# Check if timings folder exists
+		# Check if timings folder exists
-if not os.path.isdir('timings/'):
+		if not os.path.isdir('timings/'):
-    os.mkdir('timings')
+			os.mkdir('timings')
 
-# IO: Save the timing to a unique txt file
+		# IO: Save the timing to a unique txt file
-with open(f'timings/{threads}_threads_t{timestamp}.txt', 'w') as file:
+		with open(f'timings/{threads}_threads_t{timestamp}.txt', 'w') as file:
-    print(f'{threads},{elapsed_time:.6f}', file=file)
+			print(f'{threads},{elapsed_time:.6f}', file=file)

exercises/exerciseA/plot.py

@@ -1,6 +1,7 @@
 import os
 import numpy as np
 import matplotlib.pyplot as plt
+import pandas as pd
 
 # IO: This loads the timings for you 
 threads, timings = [], []
@@ -11,13 +12,23 @@ for file in os.listdir('timings'):
         timings.append(float(t))
 threads = np.array(threads)
 timings = np.array(timings)
+dat = {'timings': timings, 'threads': threads}
 
-print('This is the data I loaded: threads =', threads, ', timings =',timings)
+data = pd.DataFrame(dat)
 
+averages = data.groupby('threads').aggregate(['mean','std'])
+averages.to_csv('data.csv')
 fig, axs = plt.subplots()
 
 # CREATE YOUR PLOT HERE
 # Remember to label your axis
 # Feel free to make it pretty
+means = averages['timings']['mean']
+stds = averages['timings']['std']
 
+axs.plot(averages.index, means)
+axs.fill_between(averages.index, means-stds, means+stds, alpha=0.3)
+axs.set_xlabel('Num threads')
+axs.set_ylabel('Time (s)')
+plt.show()
 plt.savefig('threads_v_timings.png', dpi=300)