Python accelerated (using Intel® MKL)

By James R., Published: 01/03/2016, Last Updated: 01/03/2016

Python can be accelerated by having the numerical libraries, NumPy and SciPy, use the Intel® Math Kernel Library (Intel® MKL).  This requires no change to your Python application, and instantly optimizes performance on Intel processors, including Intel® Xeon® processors and Intel® Xeon Phi™ processors (codenamed Knights Landing).  Note: Intel MKL is supported as part of several Intel products, and is also is available free to anyone, under a community license.

There are several ways to do this, the easiest being simply to use a distribution which already optimizes Python libraries with Intel MKL.

Here is a list of distributions, which are available for free, which offer accelerated Python performance:

You can also build the libraries yourself to use Intel MKL.  Instructions for doing so, along with other performance oriented tuning advice/tips, can be found at  For offloading to Intel Xeon Phi coprocessors, you may be interested in pyMIC - read about it at