| Re: speed comparison of IDL, numPy, Matlab [message #23675 is a reply to message #23666] |
Mon, 12 February 2001 07:14   |
Christopher Lee
Messages: 4
Registered: February 2001
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Junior Member |
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>>>> > "Gavrie" == Gavrie <gavrie@my-deja.com> writes:
Gavrie> In article <3A7EEE5C.6419B707@pacific.jpl.nasa.gov>, Benyang
Gavrie> Tang <btang@pacific.jpl.nasa.gov> wrote:
Gavrie> So, this difference seems a bit strange, doesn't it? I'm using
Gavrie> MATLAB 6 (whichis supposed to be *slower* than 5.3), and Python
Gavrie> 1.5.2.
I believe there's an explaination for this:
While I've heard that Matlab 6's footprint is much larger, especially the
interface, I have heard that Matlab 6 does (finally) use LAPACK and BLAS
which are optimized for modern machines w/ hierarchical memory. I would
therefore expect that it would do substantially better than the
out-of-the-box numpy build for matrix multiplication. By default, numpy
uses a naive algorithm for matrix multiplication which is quite cache
unfriendly. When numpy is linked to ATLAS's BLAS routines and LAPACK, it's
more cache-friendly---and much faster.
I did some benchmarks myself: For matrix inversion of a 1000x1000 matrix,
numpy-atlas is 7 times faster than matlab 5.3 (no lapack). The difference
is greater if you have a dual processor machine because ATLAS now has
options for multi-threaded operation. And for matrix multiplication you
can compare the naive numpy multiplication with the optmized-dgemm based
version on the same matrix:
# 1000 x 1000 matrix
time elapsed for matrixmultiply (sec) 24.142714
time elapsed for dgemm-matrixmultiply (sec) 5.997188
-chris
[BTW: I'm working on putting together an optimized-numpy distribution that
uses ATLAS by default.]
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