There is a total lack of vectorization in your code, so you are right
about the lack of vectorization.
What happens is that you see the result of the Matlab JIT compiler
speeding up the loop.
With a vectorized array expression, there will hardly be any difference.
Sturla
Den 19.07.2011 11:05, skrev Carlos Becker:
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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