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MKL Optimizations

Boost the speed of popular numerical Python libraries with Intel’s Math Kernel Library (MKL)

Developed specifically for science, engineering, and financial computations, Intel™ Math Kernel Library (MKL) is a set of threaded and vectorized math routines that work to accelerate various math functions and applications. Continuum has packaged MKL-powered binary versions of some of the most popular numerical/scientific Python libraries into MKL Optimizations for improved performance.

MKL Optimizations includes:

  • Speed-boosted NumPy, SciPy, scikit-learn, and NumExpr
  • The packaging of MKL with redistributable binaries in Anaconda for easy access to the MKL runtime library.
  • Python bindings to the low level MKL service functions, which allow for the modification of the number of threads being used during runtime.

MKL Optimizations is available as an add-on to Anaconda or as part of the Anaconda Accelerate package, or behind your firewall in Anaconda Server.

Get Started with the MKL Optimizations Quick Start Guide [pdf]


MKL Optimizations Benchmarks


View the code used to create these benchmark plots.

Supported Platforms

MKL Optimizations is supported on Intel processors.

In addition to being supported on Python 2.6, 2.7, and 3.3, MKL Optimizations is available for:

  • 32-bit and 64-bit Linux
  • 32-bit and 64-bit Windows
  • Intel 64-bit Mac OS X

License Details

For $29, you will receive one license/copy of MKL Optimizations. This license entitles the licensee (a singular user) to use MKL Optimizations on as many machines as are personally used by the licensee. (For example, you CAN use MKL Optimizations on your home laptop and your office desktop.) If MKL Optimizations is to be on a cluster, or used by/available for more than one user, please contact us with the details of your situation and we will help determine the right solution for you.

MKL Optimizations End User License Agreement


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