I recently asked a question about optimizing mask functions in Matlab . I have two answers that help me a lot, but it seems that all Matlab solutions seem, in my terms, much slower than one of Numpy's solutions. The code for the various functions can be found in my previous question, but to give an idea of โโwhat I'm doing, I give the Numpy loop solution, which, of course, is not the fastest, but which may be the easiest to read:
def dealiasing2d(where, data):
nk, n0, n1 = data.shape
for i0 in xrange(n0):
for i1 in xrange(n1):
if where[i0, i1]:
data[:, i0, i1] = 0.
I get (with Matlab R2014b and the "base" Numpy 1.9.1 related to Blas and Lapack) ( n0 = n1 = N):
N = 500 ; nk = 500:
Method | time (s) | normalized
----------------|----------|------------
Numpy | 0.05 | 1.0
Numpy loop | 0.05 | 1.0
Matlab find | 0.74 | 14.8
Matlab bsxfun2 | 0.76 | 15.2
Matlab bsxfun | 0.78 | 15.6
Matlab loop | 0.78 | 15.6
Matlab repmat | 0.89 | 17.8
N = 500 ; nk = 100:
Method | time (s) | normalized
----------------|----------|------------
Numpy | 0.01 | 1.0
Numpy loop | 0.03 | 3.0
Matlab find | 0.15 | 13.6
Matlab bsxfun2 | 0.15 | 13.6
Matlab bsxfun | 0.16 | 14.5
Matlab loop | 0.16 | 14.5
Matlab repmat | 0.18 | 16.4
N = 2000 ; nk = 10:
Method | time (s) | normalized
----------------|----------|------------
Numpy | 0.02 | 1.0
Matlab find | 0.23 | 13.8
Matlab bsxfun2 | 0.23 | 13.8
Matlab bsxfun | 0.26 | 15.6
Matlab repmat | 0.28 | 16.8
Matlab loop | 0.34 | 20.4
Numpy loop | 0.42 | 25.1
, . , Numpy Matlab , , 10 ! , - , . Matlab, , . Matlab Numpy?
? , Matlab , Python ?
Matlab, :
N = 500;
n0 = N;
n1 = N;
nk = 500;
disp(['N = ', num2str(N), ' ; nk = ', num2str(nk)])
where = false([n1, n0]);
where(1:100, 1:100) = 1;
data = (5.+1i)*ones([n1, n0, nk]);
disp('time dealiasing2d_loops:')
time = timeit(@() dealiasing2d_loops(where, data));
disp([' ', num2str(time), ' s'])
disp('time dealiasing2d_find:')
time = timeit(@() dealiasing2d_find(where, data));
disp([' ', num2str(time), ' s'])
disp('time dealiasing2d_bsxfun:')
time = timeit(@() dealiasing2d_bsxfun(where, data));
disp([' ', num2str(time), ' s'])
disp('time dealiasing2d_bsxfun2:')
time = timeit(@() dealiasing2d_bsxfun2(where, data));
disp([' ', num2str(time), ' s'])
disp('time dealiasing2d_repmat:')
time = timeit(@() dealiasing2d_repmat(where, data));
disp([' ', num2str(time), ' s'])
Python
from __future__ import print_function
import numpy as np
from timeit import timeit, repeat
import_lines = {
'numpy_bad': 'from dealiasing_numpy_bad import dealiasing2d as dealiasing',
'numpy': 'from dealiasing_numpy import dealiasing'}
tools = import_lines.keys()
time_approx_one_bench = 5.
setup = """
import numpy as np
N = 500
n0, n1 = N, N
nk = 500
where = np.zeros((n0, n1), dtype=np.uint8)
where[0:100, 0:100] = 1
data = (5.+1j)*np.ones((nk, n0, n1), dtype=np.complex128)
"""
exec(setup)
print('n0 = n1 = {}, nk = {}'.format(N, nk))
print(13*' ' + 'min mean')
durations = np.empty([len(tools)])
for it, tool in enumerate(tools):
setup_tool = import_lines[tool] + setup
duration = timeit(setup_tool + 'dealiasing(where, data)', number=1)
nb_repeat = int(round((time_approx_one_bench - duration)/duration))
nb_repeat = max(1, nb_repeat)
ds = np.array(repeat('dealiasing(where, data)',
setup=setup_tool, number=1, repeat=nb_repeat))
duration = ds.min()
print('{:11s} {:8.2e} s ; {:8.2e} s'.format(
tool.capitalize() + ':', duration, ds.mean()))
durations[it] = duration
fastest = tools[durations.argmin()].capitalize()
durations = durations / durations.min()
print('Durations normalized by the fastest method (' + fastest + '):')
for it, tool in enumerate(tools):
print('{:11s} {:8.3f}'.format(tool.capitalize() + ':', durations[it]))