Hi, I started with numpy a few days ago. I was timing some array operations
and found that numpy takes 3 or 4 times longer than Matlab on a simple
array-minus-scalar operation.
This looks as if there is a lack of vectorization, even though this is just
a guess. I hope this is not reposting. I tried searching the mailing list
database but did not find anything related specifically to a problem like
this one.
Here there is the python test code:
--------------------------------------------
from datetime import datetime
import numpy as np
def test():
m = np.ones([2000,2000],float)
N = 100
t1 = datetime.now()
for x in range(N):
k = m - 0.5
t2 = datetime.now()
print (t2 - t1).total_seconds() / N
--------------------------------------------
And matlab:
--------------------------------------------
m = rand(2000,2000);
N = 100;
tic;
for I=1:N
k = m - 0.5;
end
toc / N
--------------------------------------------
I have the impression that the speed boost with Matlab is not related to
matlab optimizations, since singe-runs also render similar timings.
I tried compiling ATLAS for SSE2 and didn't observe any difference. Any
clues?
Thanks,
Carlos
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