Math Kernel Library from Intel



The Flagship High-Performance Computing Math Library for Windows*, Linux*, and Mac OS* X
Intel® Math Kernel Library (Intel® MKL) 10.3

Benefit from performance optimizations for current and future Intel® processors with math routines for science, engineering, and financial applications that require maximum performance.

 

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Power science, engineering and financial applications with this highly optimized computing math library


Intel® Math Kernel Library (Intel® MKL) is a computing math library of highly optimized, extensively threaded math routines for applications that require maximum performance. Core math functions include BLAS, LAPACK, ScaLAPACK1, sparse solvers, fast Fourier transforms, vector math, and more.

Offering performance optimizations for current and next-generation Intel® processors, it includes improved integration with Microsoft Visual Studio*, Eclipse*, and XCode*. The Intel® MKL computing math library allows for full integration of the Intel® Compatibility OpenMP* runtime library for greater Windows*/Linux* cross-platform compatibility..


ScaLAPACK1 is not supported under Mac OS* X.

 Benefits:
  • Outstanding performance - multicore and multiprocessor ready
  • Automatic parallelization
  • Standard APIs in C and Fortran
  • Royalty free redistribution
  • World-class technical support, knowledge base, and active Intel® MKL forum

For advanced performance and greater value, Intel® MKL is available in other products, including:
DGEMM on desktop processor

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LAPACK on desktop processor

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DGEMM on server processor

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LAPACK on server processor

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BLAS and LAPACK

Intel® Math Kernel Library (Intel® MKL) provides extremely well-tuned BLAS and LAPACK implementations that deliver significant performance leadership over computing math library alternatives on both desktop and server processors.

ScaLAPACK

Intel MKL includes a highly optimized version of ScaLAPACK on clusters and delivers significant performance improvements over the NETLIB* implementation on both desktop and server processors.
2D FFT on desktop processor

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3D FFT on desktop processor

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2D FFT on server processor

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3D FFT on server processor

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Fast Fourier Transforms and Cluster FFT

Intel MKL Fast Fourier Transforms (FFT) are highly optimized and provide significant performance gains on both desktop and server processor based systems compared with alternative libraries for medium and large transform sizes. FFTW interface wrappers are included. Support for distributed memory systems (clusters) is included with Cluster FFT.


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Optimized LINPACK, Improved Performance
The Intel MKL computing math library package includes an optimized implementation of the LINPACK benchmark, which is easy to run on any Intel® architecture platform. It provides the best performance on the latest Intel® processors, getting close to the maximum Gflops supported by the underlying platform.

 

 


Vector Random Number Generators
Intel MKL Vector Statistical Library (VSL) is a collection of nine random number generators and 22 probability distributions that deliver significant performance improvements in physics, chemistry, and financial analysis.


Vector Math Library

Intel MKL provides vector implementations of computationally intensive core mathematical functions.


To learn more about Intel Math Kernel Library, download the product brief ›

What's new in Intel® Math Kernel Library 10.3


Support for Intel® Advanced Vector Extensions (Intel® AVX) to SSE

  • Faster floating point operations in BLAS, LAPACK, FFTs, VML and VSL functional domains on the upcoming Sandy Bridge processor

C interfaces for LAPACK and PARDISO for easier use by C developers

  • C LAPACK interfaces supporting row-major ordering and support for c-style (zero-based) array indexing for PARDISO arrays

New Intel® Summary Statistics Library

  • New domain covering a broad range of statistics functions

Dynamic accuracy control for VML

  • New interfaces for all VML functions that include parameters for setting accuracy mode

Additional optimizations for BLAS, LAPACK, PARDISO, FFTs, and VSL

  • Delivers increased performance for many algorithms

Prof. Jack Dongarra, University of Tennessee, Knoxville, Innovative Computing Laboratory
"University of Tennessee, Knoxville: "The Intel® Math Kernel Library is indispensable for any high performance computer user on x86 platforms. It provides a rich, highly optimized collection of math routines, including our own BLAS, LAPACK, and ScaLAPACK in addition to other basic functions such as sparse matrix operations and FFTs. Outstanding performance is achieved on both multicore and multiprocessor systems."

Dr Antoine Petitet, Ph.D, HPC Lead , ESI Computational Structural Mechanics Group
"ESI Computational Structural Mechanics Group: "PAM-CRASH and PAM-STAMP rely on the performance of the Intel® Math Kernel Library 10.0. We are delighted with the results in terms of both memory use and SMP performance."

Gilles Artaud, Head of Quantitative Research, Risk and Permanent Control, CALYON
"CALYON counter party risk is computed using complex numerical algorithms, distributed on a cluster of hundreds of Intel processors. We are constantly facing performance issues. The Intel® MKL includes vectors and matrixes calculation, FFT, random generators, and many other algorithms which allowed substantial performance improvements, sometimes up to a hundred times, and reduced development costs."

Chris Reid, Vice President of Marketing, ANSYS, Inc.
"Intel MKL helps ANSYS achieve excellent performance on Intel processors and has powered our engineering simulation software for more than 10 years. Intel multi-core processors coupled with the Intel MKL library help us deliver high performance, with engineered scalability from workstations to server systems. The continued optimization of Intel MKL ensures the best performance for ANSYS software users on the latest generation Intel processors."

Alistair Downie, Paradigm Geophysical
"Paradigm use Intel® MKL in our technical solutions for our oil and gas customers because it provides outstanding performance. Our HPC algorithms rely on Intel MKL being tuned, thread safe and multicore aware across Intel's processor families. Using Intel MKL enables us to focus our time and effort on our solutions, yet still deliver optimal performance across a range of systems, from lap tops and workstations to HPC clusters."
Intel® Math Kernel Library (Intel® MKL) Support

Browse the Intel® Math Kernel Library Knowledge Base

Supported Linux* Distributions



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