performance.py
8ecef19fff6bancarola

performance.py
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			#!/usr/bin/python

			import os
			import numpy as np
			from numpy import array

			testdir = "tests"
			os.system("mkdir -p " + testdir)

			Ntr = [1, 2, 4, 8, 16]
			#sample = "Running the algorithm with 4 threads on 10000 by 10000 array for 5 iterations took 1.547 seconds"

			def loadtimes(fstream):
			t = []
			for line in fstream:
			# skip if no information is contained
			if not line.startswith("Running the algorithm"):
			continue

			values = line.split(' ') # split into string set
			values = [i for i in values if i != ''] # fliter null strings
			t.append(float(values[15])) # 16-th value should contain the elapsed time
			return t

			T = [] # times
			S = [] # speedups
			for filename in os.listdir(os.getcwd() + '/' + testdir):
			with open(os.path.join(os.getcwd() + '/' + testdir, filename), 'r') as f:
			t = np.array(loadtimes(f))
			T.append(t)
			t0 = t[0]
			S.append(t0 / t)

			Tmean = np.mean(T, axis=0).tolist()
			Tsq = np.sqrt(np.var(T, axis=0)).tolist() # compute mean and variance of arrays

			Smean = np.mean(S, axis=0).tolist()
			Ssq = np.sqrt(np.var(S, axis=0)).tolist() # compute mean and variance of arrays

			# output values
			out = open("times.txt", 'w')
			print("ntr t dt s ds", file=out)
			for (ntr, t, dt, s, ds) in zip(Ntr, Tmean, Tsq, Smean, Ssq):
			print("%d %.3g %.3g %.3g %.3g" % (ntr, t, dt, s, ds), file=out)
			out.close()