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.

 

Topics Description Third Party Application/Tool
Numpy/Scipy with Intel® MKL This article intended 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

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

These guides 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 helps the Intel® Math Kernel Library (Intel® MKL) customers 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#
Using Intel® Math Kernel Library and Intel® Integrated Performance Primitives in the Microsoft* .NET* Framework This document explains how to call the Intel® Math Kernel Library (Intel® MKL) and Intel® Integrated Performance Primitives (Intel® IPP) from .NET Framework languages such as C#. .NET Framework
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*
Building MPICH1 with GNU Fortran MPICH1 does not work with GNU Fortran out-of-the-box - this article describes the modifications that must be made for it to work properly. GNU Fortran
C++ template math libraries This article provides information about existing high-level C++ APIs available to invoke MKL functionality.  

 

 

Einzelheiten zur Compiler-Optimierung finden Sie in unserem Optimierungshinweis.