Created
May 10, 2016 05:19
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| import matplotlib.pyplot as plt | |
| import timeit | |
| import hashlib | |
| import gc | |
| import numpy as np | |
| import numexpr as ne | |
| from a import a as pythran | |
| from numba import vectorize, jit | |
| from pythran import compile_pythrancode | |
| from imp import load_dynamic | |
| orders_n = [10**n for n in range(1, 5)] | |
| def make_numpy(func): | |
| return 'lambda x, y: ' + func | |
| def make_numexpr(func): | |
| return 'lambda x, y: ne.evaluate("' + func + '")' | |
| def make_numba(func): | |
| return 'jit(' + make_numpy(func) + ')' | |
| def make_pythran(func): | |
| m = hashlib.md5() ; m.update(func) ; name = m.hexdigest() | |
| code = '#pythran export foo(float64[][], float64[][])\ndef foo(x, y): return ' + func | |
| so = compile_pythrancode('pythran_' + name, code) | |
| globals()['pythran_kernel'] = load_dynamic('pythran_' + name, so).foo | |
| return 'pythran_kernel' | |
| expressions = ( | |
| 'x + y', | |
| '.8 * x + .2 * y', | |
| 'x[:] + y [:]', | |
| 'x + 1', | |
| 'x + y[0]', | |
| 'x[1:-1] + y[1:-1]', | |
| 'x[1:-1,1:-1] + y[1:-1,1:-1]', | |
| 'x[1:,1:] + x[:-1,1:] + x[1:,:-1] + x[:-1,:-1]', | |
| '(x - y)**2 / (x + y)', | |
| '.1 * x**2 + .2 * y ** 2 + .3 * x * y + .4 * x + .5 * y + .6', | |
| ) | |
| engines = ( | |
| 'numpy', | |
| 'numexpr', | |
| 'pythran', | |
| 'numba', | |
| ) | |
| for nth, expression in enumerate(expressions): | |
| for engine in engines: | |
| locals()[engine] = eval('eval(make_' + engine + '(expression))') | |
| timings = {f:[] for f in engines} | |
| for n in orders_n: | |
| for f in engines: | |
| A = np.random.rand(n,n) | |
| B = np.random.rand(n,n) | |
| try: | |
| timing = min(timeit.Timer('%s(A, B)' %f, | |
| 'from __main__ import A, B, %s' %f) | |
| .repeat(repeat=3, number=10)) | |
| except TypeError: | |
| timing = 0 | |
| timings[f].append(timing) | |
| del A | |
| del B | |
| gc.collect() | |
| def plot_figures(engines): | |
| fig = plt.figure(figsize=(8,8)) | |
| for f in engines: | |
| plt.plot([i**2 for i in orders_n], [i/n for i,n in zip(timings[f], timings['numpy'])], | |
| alpha=0.5, label='%s' %f, marker='o', lw=2) | |
| plt.legend(loc='upper left') | |
| #plt.xscale('log') | |
| #plt.yscale('log') | |
| plt.axis(ymax=2.0,ymin=0.) | |
| plt.grid() | |
| plt.xlabel('number of elements per matrix') | |
| plt.ylabel('normalized execution time (w/ numpy) lower is better') | |
| plt.title('Approaches to evaluate ' + expression) | |
| plt.savefig('plot{}.png'.format(nth)) | |
| plot_figures(engines) |
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