[Numpy-discussion] Fast threading solution thoughts

Michael Abshoff michael.abshoff@googlemail....
Thu Feb 12 08:34:58 CST 2009

David Cournapeau wrote:
> Matthieu Brucher wrote:
>> For BLAS level 3, the MKL is parallelized (so matrix multiplication is).

Hi David,

> Same for ATLAS: thread support is one focus in the 3.9 serie, currently
> in development. 

ATLAS has had thread support for a long, long time. The 3.9 series just 
improves it substantially by using affinity when available and removes 
some long standing issues with allocation performance that one had to 
work around before by setting some defines at compile time.

 > I have never used it, I don't know how it compare to the
> MKL,

It does compare quite well and is more or less on par with the latest 
MKL releases in the 3.9 series. 3.8.2 is maybe 10% to 15% slower on i7 
as well as Core2 cores than the MKL. On big advantage of ATLAS is that 
it tends to work when using it with numpy/scipy unlike the Intel MKL 
where one has to work around a bunch of oddities and jump through hoops 
to get it to work. It seems that Intel must rename at least one library 
in each release of the MKL to keep build system maintainers occupied :)

The big disadvantage of ATLAS is that Windows support is currently 
limited to 32 bits, but 3.9.5 and higher have SFU/SUA support, so 64 bit 
support is possible. Clint told me the main issue here was lack of 
access and it isn't too high on his priority list.

> David



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