[Numpy-discussion] building numpy with atlas on ubuntu edgy
Wed Apr 18 14:36:32 CDT 2007
Keith Goodman <firstname.lastname@example.org> [2007-04-18 10:49]:
> I'd like to compile atlas so that I can take full advantage of my core
> 2 duo.
If your use is entirely non-commercial you can use Intel's MKL with
built-in optimized BLAS and LAPACK and avoid the need for ATLAS.
The BLAS and LAPACK libraries are time-honored standards for solving a
large variety of linear algebra problems. The Intel� Math Kernel Library
(Intel� MKL) contains an implementation of BLAS and LAPACK that is
highly optimized for Intel� processors. Intel MKL can enable you to
achieve significant performance improvements over alternative
implementations of BLAS and LAPACK.
The charts immediately below show that, for Itanium� 2-based systems,
Intel MKL performs approximately 20 percent faster than ATLAS for large
matrices, and even faster for small matrices. On the new Dual-Core
Intel� Xeon� processors, Intel MKL provides similar performance
I've compiled both Python (icc) and Numpy using icc 9.1 and MKL
9.1_beta. It's significantly faster than using gcc on my Core 2 Duo
system. I'm still looking for a broad performance test (something like
The best compiler flags I've found are: -fast -parallel
In some cases -funroll-loops and -fno-alias helps.
Time flies like wind. Fruit flies like pears.
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