Keith Goodman <[EMAIL PROTECTED]> [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. http://www.intel.com/cd/software/products/asmo-na/eng/266858.htm 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 advantages. 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 Scimark, say). The best compiler flags I've found are: -fast -parallel In some cases -funroll-loops and -fno-alias helps. -rex -- Time flies like wind. Fruit flies like pears. _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion