AOS-Project/utils/plot.py

#!/usr/bin/env python
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches

# Import data from file
data_cpu = np.genfromtxt('results/cpu/average.csv', dtype=None, delimiter=',', names=['name', 'time', 'power'])
data_gpu_primary = np.genfromtxt('results/gpu-primary/average.csv', dtype=None, delimiter=',', names=['name', 'time', 'power'])
data_gpu_secondary = np.genfromtxt('results/gpu-secondary/average.csv', dtype=None, delimiter=',', names=['name', 'time', 'power'])

# We construct a set in which we insert each benchmark name once
nameset = set()
names = data_cpu['name']
for name in names:
    nameset.add(name)

# Generate an array as placeholder for the x axis (we need to pass from a list to an array to take advantage of range)
x = range(0, 17)
# Strip away the "opencl/" prefix from all the name
stripped_names = []
for elem in data_cpu['name']:
    elem = elem.replace("opencl/", "")
    elem = elem.replace("/ocl", "")
    elem = elem.replace("/OpenCL", "")
    stripped_names.append(elem)


# Create the bar plot for the time values
plt.bar(x, data_cpu['time'], width=0.3, color='b', align='edge')
plt.bar(x, data_gpu_primary['time'], width=-0.3, color='r', align='center')
plt.bar(x, data_gpu_secondary['time'], width=-0.3, color='g', align='edge')
plt.xticks(x, stripped_names)
plt.title('Execution time of the various benchmarks expressed in seconds')
plt.xlabel('Benchmark')
plt.ylabel('seconds')

# Add some patches as legend of the colors used for the various benchmarks
red_patch = mpatches.Patch(color='blue', label='Execution time for cpu')
blue_patch = mpatches.Patch(color='red', label='Execution time for gpu(4 core)')
green_patch = mpatches.Patch(color='green', label='Execution time for gpu(2 core)')

plt.legend(handles=[red_patch, blue_patch, green_patch])

# Save the obtained plot on file
plt.savefig('times.pdf')
plt.show()


# Create the bar plot for the power values
plt.bar(x, data_cpu['power'], width=0.3, color='b', align='edge')
plt.bar(x, data_gpu_primary['power'], width=-0.3, color='r', align='center')
plt.bar(x, data_gpu_secondary['power'], width=-0.3, color='g', align='edge')
plt.xticks(x, stripped_names)
plt.title('Power consumption of the various benchmarks expressed in Watt/hour')
plt.xlabel('Benchmark')
plt.ylabel('Watt/hour')

# Add some patches as legend of the colors used for the various benchmarks
red_patch = mpatches.Patch(color='blue', label='Power consumption for cpu')
blue_patch = mpatches.Patch(color='red', label='Power consumption for gpu(4 core)')
green_patch = mpatches.Patch(color='green', label='Execution time for gpu(2 core)')

plt.legend(handles=[red_patch, blue_patch, green_patch])

# Save the obtained plot on file
plt.savefig('power.pdf')
plt.show()