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.
Please note: The NumPy and SciPy packages on Windows in the (free) Anaconda Python distribution are statically linked to MKL. However, even on Windows you can benefit from MKL Optimizations with consistent dynamic linkage to MKL, as well as the MKL-linked Python bindings for scikit-learn and NumExpr.
Get Started with the MKL Optimizations Quick Start Guide [pdf]
MKL Optimizations Benchmarks
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 (Python 2.6 and 2.7 only)
For $29.00, you will receive one MKL Optimizations license. You are entitled to any updates issued to this package for a period of one year.
Although this license is persistent and permanent, access to package upgrades will expire after one year. You may choose to renew your license in order to access subsequent upgrades, or you may continue to use your existing copy of MKL Optimizations without upgrading.
This license entitles the licensee (a singular user) to use MKL Optimizations on as many machines as are personally used by the licensee and up to 4 nodes within a cluster. (For example, you CAN use MKL Optimizations on your home laptop and your office desktop.)
If MKL Optimizations is to be used on a cluster larger than 4 nodes, or used by/available for more than one user, please contact us and we will help determine the right solution for you.
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