Intel® MKL with NumPy, SciPy, MATLAB, C#, Python, NAG and More

By Gennady Fedorov,

Published:05/08/2012   Last Updated:12/26/2017

The following table lists links to the useful articles describing how Intel® Math Kernel Library (Intel® MKL) can be used with third-party libraries and applications.

 

Topics Description Third Party Application/Tool
NumPy/SciPy with Intel® MKL This article intends to help current NumPy/SciPy users to take advantage of Intel® Math Kernel Library (Intel® MKL). Numpy/Scipy
Using Intel® MKL in R This article shows how to configure R to use the optimized BLAS and LAPACK in the Intel® Math Kernel Library (Intel® MKL). R
Using Intel® MKL in Gromacs This article helps the current Gromacs* users get better performance by utilizing the Intel® Math Kernel Library (Intel® MKL). It explains how to build 64-bit Gromacs* with Intel MKL for Intel® 64 based applications. Gromacs*
Using Intel® MKL in GNU Octave This article helps the current GNU Octave* users to incorporate the latest versions of the Intel® Math Kernel Library (Intel® MKL)  on Linux* platforms on Intel® Xeon® *processor-based systems. Octave*
Using Intel MKL BLAS and LAPACK with PETSc This article describes how to build the Portable Extensible Toolkit for Scientific Computation (PETSc) with the Intel® Math Kernel Library (Intel® MKL) BLAS and LAPACK. PETSc
Using Intel® MKL in your Python program This article describes how to use the Intel® Math Kernel Library (Intel® MKL) from a Python* program Python*
Performance hints for WRF on Intel® architecture This article explains how to configure the Weather Research & Forecasting (WRF) run-time environment to achieve the best performance and scalability on Intel® architecture with Intel® software tools. Weather Research & Forecasting (WRF) Application

HPL application note

This guide helps the current HPL (High Performance LINPACK) users get better benchmark performance by utilizing Intel® Math Kernel Library (Intel® MKL) BLAS High Performance Computing Challenge benchmarks

Using Intel MKL with MATLAB

Using Intel® MKL in MATLAB Executable (MEX) Files

These guides help the Intel® Math Kernel Library (Intel® MKL) customers to use the latest version of Intel® MKL for Windows* OS with the MathWorks* MATLAB*. MATLAB*
Using Intel® MKL with IMSL* Fortran numerical library This article explains how to use the latest version of the Intel® Math Kernel Library (Intel® MKL) with IMSL* Fortran Numerical Library Version 6.0.0 on Intel® architecture systems under Microsoft Windows* systems IMSL* Fortran
Using Intel® MKL with the NAG* libraries This article describes how to use the Intel® Math Kernel Library (Intel® MKL) with the NAG* libraries. Currently, Intel MKL is used for NAG's BLAS and LAPACK functionalities, with the addition of FFTs for NAG's Fortran SMP Libraries. NAG* libraries

Using Intel® MKL in your C# program

Some more additional tips "How to call MKL from your C# code"

These articles describe how to call and link the Intel® Math Kernel Library (Intel® MKL) functions from your C# code. Examples are provided for calling the BLAS, LAPACK, DFTI (the FFT interface), the PARDISO direct sparse solver, and the vector math library (VML). C#
How do I use Intel® MKL with Java*? The Intel® Math Kernel Library (Intel® MKL) package contains a set of examples that demonstrate the use of Intel MKL functions with Java*. The Java example set includes Java Native Interface (JNI) wrappers. These Java examples are intended for tutorial use only. Java*
C++ template math libraries This article provides information about existing high-level C++ APIs available to invoke MKL functionality.  
     


 

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.