I built on a 64 bit machine with the Intel suite version 12.1.3 - newer versions of the Intel suite have troubles with respect to resolving library dependencies (which is the topic of this blog post). I just updated the installation to the most recent Python 2 (2.7.6), numpy 1.8.0 and scipy 0.13.3. use numpy’s data types and numpy’s/scipy’s functions the right way, then - spoken in general terms - no commercial or other open source software package is able to squeeze more performance from your hardware. If you get the build right, and write proper code, i.e. As far as I know, and also from my own experience, this combination of tools lets you get close-to-optimum performance for numerical simulations on classical hardware, especially for linear algebra calculations. We have built numpy and scipy against Intel’s MKL, with Intel’s C++ compiler icc and Intel’s Fortran compiler ifort. ![]() At work we make heavy use of the Python/numpy/scipy stack.
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