Add a barplot

bin/barplot.py

+import itertools
+import pandas
+import matplotlib
+import sys
+
+matplotlib.use("PDF")
+
+import matplotlib.pyplot as plt
+
+filename = sys.argv[1]
+fig, ax = plt.subplots()
+width = 0.5
+
+opframe = pandas.read_csv("./operations.csv",
+                          header=None,
+                          delimiter=" ",
+                          names=("exec", "degree", "what", "ops"),
+                          )
+
+av = opframe[opframe.what == "alpha_volume_kernel"]
+ask = opframe[opframe.what == "alpha_skeleton_kernel"]
+ab = opframe[opframe.what == "alpha_boundary_kernel"]
+deg = [i - 0.3 for i in sorted(av['degree'])]
+
+_, y1 = list(itertools.izip(*sorted(itertools.izip(av['degree'], av['ops']))))
+_, y2 = list(itertools.izip(*sorted(itertools.izip(ask['degree'], ask['ops']))))
+y2 = [a+b for a, b in zip(y1, y2)]
+_, y3 = list(itertools.izip(*sorted(itertools.izip(ab['degree'], ab['ops']))))
+y3 = [a+b for a, b in zip(y2, y3)]
+y4 = [1.0] * len(deg)
+
+r4 = ax.bar(deg, y4, width, color="grey") 
+r3 = ax.bar(deg, y3, width, color="green") 
+r2 = ax.bar(deg, y2, width, color="blue") 
+r1 = ax.bar(deg, y1, width, color="red")
+
+ax.set_ylabel("Percentage")
+ax.set_xticks(sorted(av['degree']))
+ax.set_xticklabels(["Q{}".format(k) for k in sorted(av['degree'])])
+
+timeframe = pandas.read_csv("./timeratios.csv",
+                            header=None,
+                            delimiter=" ",
+                            names=("exec", "degree", "what", "ops"),
+                            )
+
+av = timeframe[timeframe.what == "alpha_volume_kernel"]
+ask = timeframe[timeframe.what == "alpha_skeleton_kernel"]
+ab = timeframe[timeframe.what == "alpha_boundary_kernel"]
+deg = [i + 0.3 for i in sorted(av['degree'])]
+
+_, y1 = list(itertools.izip(*sorted(itertools.izip(av['degree'], av['ops']))))
+_, y2 = list(itertools.izip(*sorted(itertools.izip(ask['degree'], ask['ops']))))
+y2 = [a+b for a, b in zip(y1, y2)]
+_, y3 = list(itertools.izip(*sorted(itertools.izip(ab['degree'], ab['ops']))))
+y3 = [a+b for a, b in zip(y2, y3)]
+y4 = [1.0] * len(deg)
+
+r4 = ax.bar(deg, y4, width, color="grey", label="PDELab overhead") 
+r3 = ax.bar(deg, y3, width, color="green", label="Boundary integrals") 
+r2 = ax.bar(deg, y2, width, color="blue", label="Skeleton integrals") 
+r1 = ax.bar(deg, y1, width, color="red", label="Volume integrals")
+
+ax.legend(loc=3,
+          ncol=2,
+          bbox_to_anchor=(0.1, 1., 1., .1),
+          )
+
+for x in av['degree']:
+    ax.text(x, -0.09, "Flops", rotation=45, horizontalalignment="right")
+    ax.text(x + 0.6, -0.09, "Time", rotation=45, horizontalalignment="right")
+
+plt.savefig(filename)

bin/process_measurements.py

@@ -14,6 +14,36 @@ def join_csv_files():
                     out.write("{} {}".format(match.group(1), line))
 
 
+def get_reference_kernel(kernel):
+    if kernel.startswith("alpha"):
+        return "residual_evaluation"
+    if kernel.startswith("jacobian_apply"):
+        return "apply_jacobian"
+    return kernel
+
+
+def calculate_operations_percentage():
+    frame = pandas.read_csv('timings.csv', header=None, names=('rank', 'ident', 'kernel', 'what', 'value'), delimiter=' ')
+    ops = frame[frame.what != "time"]
+    ops = ops.groupby(('rank', 'ident', 'kernel'))['value'].max().to_frame().reset_index().groupby(('ident', 'kernel'))['value'].max()
+    with open('operations.csv', 'w') as out:
+        for key in ops.keys():
+            ident, kernel = key
+            degree = re.match(".*deg([0-9]*).*", ident).group(1)
+            out.write(" ".join([ident, degree, kernel, str(ops[ident][kernel] / ops[ident][get_reference_kernel(kernel)]) + "\n"]))
+
+
+def calculate_times_percentage():
+    frame = pandas.read_csv('timings.csv', header=None, names=('rank', 'ident', 'kernel', 'what', 'value'), delimiter=' ')
+    time = frame[frame.what == "time"]
+    time = time.groupby(('rank', 'ident', 'kernel'))['value'].min().to_frame().reset_index().groupby(('ident', 'kernel'))['value'].max()
+    with open('timeratios.csv', 'w') as out:
+        for key in time.keys():
+            ident, kernel = key
+            degree = re.match(".*deg([0-9]*).*", ident).group(1)
+            out.write(" ".join([ident, degree, kernel, str(time[ident][kernel] / time[ident][get_reference_kernel(kernel)]) + "\n"]))
+
+
 def calculate_floprate():
     frame = pandas.read_csv('timings.csv', header=None, names=('rank', 'ident', 'kernel', 'what', 'value'), delimiter=' ')
     time = frame[frame.what == "time"]
@@ -48,3 +78,5 @@ if __name__ == '__main__':
     join_csv_files()
     calculate_floprate()
     calculate_doftimes()
+    calculate_operations_percentage()
+    calculate_times_percentage()

bin/setup.py

@@ -16,4 +16,5 @@ setup(name='perftoolcoveniencescripts',
       scripts=['performance_regression.py',
                'plot_measurements.py',
                'process_measurements.py',
+               'barplot.py',
                ])