Intel® MKL with Numpy, Scipy, Matlab, C#, Python, NAG and more

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

 

TopicsDescriptionThird Party Application/Tool
Numpy/Scipy with Intel® MKLThis article intends to help current NumPy/SciPy users to take advantage of Intel® Math Kernel Library (Intel® MKL).Numpy/Scipy
Using Intel® MKL in RThis 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 GromacsThis 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 OctaveThis 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 PETScThis 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 programThis article describes how to use the Intel® Math Kernel Library (Intel® MKL) from a Python* programPython*
Performance hints for WRF on Intel® architectureThis 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

Use of Intel® MKL in High Performance Computing Challenge (HPCC) benchmark

These guides help the current HPL (High Performance LINPACK) users get better benchmark performance by utilizing Intel® Math Kernel Library (Intel® MKL) BLASHigh 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 libraryThis 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* systemsIMSL* Fortran
Using Intel® MKL with the NAG* librariesThis 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 librariesThis article provides information about existing high-level C++ APIs available to invoke MKL functionality. 
   


 

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