Changes to the plot and analyze scripts

Quick changes to the analyze and plot scripts to plot the data regarding
the benchmarks run on a X86 platform with pocl, only on CPU

utils/analyze.py

@@ -8,7 +8,7 @@ import math
 import csv
 
 # List containing all the platform names
-platforms = ['cpu', 'gpu-primary', 'gpu-secondary']
+platforms = ['cpu']
 
 # Function that takes as parameter a platform and computes averages and variance of the results, and saves them in two dedicated files (averages.dat and variances.dat)
 def computeAverages( platform ):

utils/plot-X86.py

@@ -0,0 +1,104 @@
+#!/usr/bin/env python
+import numpy as np
+import matplotlib.mlab as mlab
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+import seaborn
+seaborn.set()
+import os
+
+# Helper function to attach labels to the bars
+def autolabel(subplot, bars):
+    for bar in bars:
+        height = bar.get_height()
+        subplot.text(bar.get_x() + bar.get_width()/2., 1.05*height, '%d' % int(height), ha='center', va='bottom')
+
+# Helper function to strip away the "opencl/" prefix and other stuff from all the names
+def stripNames (category, names):
+    stripped_names = []
+    for elem in names:
+        elem = elem.replace("opencl/", "")
+        elem = elem.replace("/ocl", "")
+        elem = elem.replace("/OpenCL", "")
+        stripped_names.append(elem)
+    return stripped_names
+
+# List containing all the platform names
+platforms = ['cpu']
+
+# List containing the categories of the benchamrks
+categories = ['short', 'long']
+
+averageTotal = {}
+stderrTotal = {}
+
+# Import data from file
+averageTotal['cpu'] = np.genfromtxt('results/cpu/average.csv', dtype=None, delimiter=',', names=['name', 'time', 'power'])
+stderrTotal['cpu'] = np.genfromtxt('results/cpu/stderr.csv', dtype=None, delimiter=',', names=['name', 'time', 'power'])
+
+# Create the folder where to store the charts, if it doesn't already exist
+if not os.path.exists('charts'):
+    os.makedirs('charts')
+
+# Dict that contains the indexes(entry in the data files) of the benchmarks taking into account their category
+indexes = {'short': [], 'long': []}
+
+# We cycle on the benchmarks results for the cpu (since usually they are the ones with the longest run time) and divide in two groups the benchmarks, putting the relative indexes in two lists
+for i in range(0,17):
+    record = averageTotal['cpu'].take(i)
+    if record['time'] < 10:
+        indexes['short'].append(i)
+    else:
+        indexes['long'].append(i)
+
+
+# We instantiate a new dict to contain the average and the std err of the measurements on the various platforms
+average = {'cpu': {}}
+stderr = {'cpu': {}}
+
+for platform in platforms:
+    for category in categories:
+        average[platform][category] = averageTotal[platform].take(indexes[category])
+        stderr[platform][category] = stderrTotal[platform].take(indexes[category])
+
+# 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 = {}
+x['short'] = np.arange(len(indexes['short']))
+x['long'] = np.arange(len(indexes['long']))
+
+# Strip from the names of the benchmarks unwanted parts, taking advantage of the helper function defined before
+x_names_stripped = {'short': [], 'long': []}
+for category in categories:
+    x_names = averageTotal['cpu'].take(indexes[category])['name'].tolist()
+    x_names_stripped[category] = stripNames(category, x_names)
+
+# Helper function that create the plots of the execution time, takes as parameter the category of the benchmark we want to plot
+def plotTimes (category):
+
+    # Initialization of the figures
+    fig, ax = plt.subplots(figsize=(20, 10))
+
+    # Create the bar plot for the time values
+    time_cpu_bars = ax.bar(x[category], average['cpu'][category]['time'], width=0.2, color='b', align='center', yerr=stderr['cpu'][category]['time'])
+
+    # Change the labels of the x axis to contain the names of the benchmarks
+    ax.set_xticks(x[category])
+    ax.set_xticklabels(x_names_stripped[category])
+    ax.set_title('Execution time of the various benchmarks expressed in seconds')
+    ax.set_xlabel('Benchmark')
+    ax.set_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')
+
+    ax.legend(handles=[red_patch])
+
+    # Invoke the helper function to attach labels for each bar
+    autolabel(ax, time_cpu_bars)
+
+    # Save the obtained plot on file
+    plt.savefig('charts/times-' + category + '.pdf')
+
+# Invoke the helper functions to plot the data
+for category in categories:
+    plotTimes(category)

utils/plot.py

@@ -10,8 +10,8 @@ import os
 # Helper function to attach labels to the bars
 def autolabel(subplot, bars):
     for bar in bars:
-        height = bar.get_height()
-        subplot.text(bar.get_x() + bar.get_width()/2., 1.05*height, '%d' % int(height), ha='center', va='bottom')
+        height = round(bar.get_height(), 1)
+        subplot.text(bar.get_x() + bar.get_width()/2., 1.05*height, '%s' % height, ha='center', va='bottom')
 
 # Helper function to strip away the "opencl/" prefix and other stuff from all the names
 def stripNames (category, names):
@@ -24,7 +24,7 @@ def stripNames (category, names):
     return stripped_names
 
 # List containing all the platform names
-platforms = ['cpu', 'gpu-primary', 'gpu-secondary']
+platforms = ['cpu']
 
 # List containing the categories of the benchamrks
 categories = ['short', 'long']
@@ -34,11 +34,7 @@ stderrTotal = {}
 
 # Import data from file
 averageTotal['cpu'] = np.genfromtxt('results/cpu/average.csv', dtype=None, delimiter=',', names=['name', 'time', 'power'])
-averageTotal['gpu-primary'] = np.genfromtxt('results/gpu-primary/average.csv', dtype=None, delimiter=',', names=['name', 'time', 'power'])
-averageTotal['gpu-secondary'] = np.genfromtxt('results/gpu-secondary/average.csv', dtype=None, delimiter=',', names=['name', 'time', 'power'])
 stderrTotal['cpu'] = np.genfromtxt('results/cpu/stderr.csv', dtype=None, delimiter=',', names=['name', 'time', 'power'])
-stderrTotal['gpu-primary'] = np.genfromtxt('results/gpu-primary/stderr.csv', dtype=None, delimiter=',', names=['name', 'time', 'power'])
-stderrTotal['gpu-secondary'] = np.genfromtxt('results/gpu-secondary/stderr.csv', dtype=None, delimiter=',', names=['name', 'time', 'power'])
 
 
 # Create the folder where to store the charts, if it doesn't already exist
@@ -51,15 +47,15 @@ indexes = {'short': [], 'long': []}
 # We cycle on the benchmarks results for the cpu (since usually they are the ones with the longest run time) and divide in two groups the benchmarks, putting the relative indexes in two lists
 for i in range(0,17):
     record = averageTotal['cpu'].take(i)
-    if record['time'] < 30:
+    if record['time'] < 10:
         indexes['short'].append(i)
     else:
         indexes['long'].append(i)
 
 
 # We instantiate a new dict to contain the average and the std err of the measurements on the various platforms
-average = {'cpu': {}, 'gpu-primary': {}, 'gpu-secondary': {}}
-stderr = {'cpu': {}, 'gpu-primary': {}, 'gpu-secondary': {}}
+average = {'cpu': {}}
+stderr = {'cpu': {}}
 
 for platform in platforms:
     for category in categories:
@@ -84,9 +80,7 @@ def plotTimes (category):
     fig, ax = plt.subplots(figsize=(20, 10))
 
     # Create the bar plot for the time values
-    time_cpu_bars = ax.bar(x[category]-0.3, average['cpu'][category]['time'], width=0.2, color='b', align='edge', yerr=stderr['cpu'][category]['time'])
-    time_gpu_primary_bars = ax.bar(x[category], average['gpu-primary'][category]['time'], width=0.2, color='r', align='center', yerr=stderr['gpu-primary'][category]['time'])
-    time_gpu_secondary_bars = ax.bar(x[category]+0.3, average['gpu-secondary'][category]['time'], width=-0.2, color='g', align='edge', yerr=stderr['gpu-secondary'][category]['time'])
+    time_cpu_bars = ax.bar(x[category], average['cpu'][category]['time'], width=0.2, color='b', align='center', yerr=stderr['cpu'][category]['time'])
 
     # Change the labels of the x axis to contain the names of the benchmarks
     ax.set_xticks(x[category])
@@ -97,53 +91,15 @@ def plotTimes (category):
 
     # 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)')
 
-    ax.legend(handles=[red_patch, blue_patch, green_patch])
+    ax.legend(handles=[red_patch])
 
     # Invoke the helper function to attach labels for each bar
     autolabel(ax, time_cpu_bars)
-    autolabel(ax, time_gpu_primary_bars)
-    autolabel(ax, time_gpu_secondary_bars)
 
     # Save the obtained plot on file
     plt.savefig('charts/times-' + category + '.pdf')
 
-# Helper function that create the plots of the execution time, takes as parameter the category of the benchmark we want to plot
-def plotPower (category):
-
-    # Create a new figure
-    fig, bx = plt.subplots(figsize=(20, 10))
-
-    # Create the bar plot for the power values
-    power_cpu_bars = bx.bar(x[category]-0.3, average['cpu'][category]['power'], width=0.2, color='b', align='edge', yerr=stderr['cpu'][category]['power'])
-    power_gpu_primary_bars = bx.bar(x[category], average['gpu-primary'][category]['power'], width=0.2, color='r', align='center', yerr=stderr['gpu-primary'][category]['power'])
-    power_gpu_secondary_bars = bx.bar(x[category]+0.3, average['gpu-secondary'][category]['power'], width=-0.2, color='g', align='edge', yerr=stderr['gpu-secondary'][category]['power'])
-
-    # Change the labels of the x axis to contain the names of the benchmarks
-    bx.set_xticks(x[category])
-    bx.set_xticklabels(x_names_stripped[category])
-    bx.set_title('Power consumption of the various benchmarks expressed in mWatt/hour')
-    bx.set_xlabel('Benchmark')
-    bx.set_ylabel('mWatt/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)')
-
-    bx.legend(handles=[red_patch, blue_patch, green_patch])
-
-    # Invoke the helper function to attach labels one for each bar
-    autolabel(bx, power_cpu_bars)
-    autolabel(bx, power_gpu_primary_bars)
-    autolabel(bx, power_gpu_secondary_bars)
-
-    # Save the obtained plot on file
-    plt.savefig('charts/power-' + category + '.pdf')
-
 # Invoke the helper functions to plot the data
 for category in categories:
     plotTimes(category)
-    plotPower(category)