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.
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
Powered by Intel
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