Outstanding performance on Intel® processors
Achieve outstanding performance with the math library that is highly optimized for Intel® Itanium® 2, Intel® Xeon®, Intel® Pentium® 4, and Intel® Core™2 Duo processor-based systems. Special attention has been paid to optimizing multi-threaded performance for the new Quad-Core Intel® Xeon® processor 5300 series and the Intel® Core™ i7. Intel MKL performance is competitive with that of other math software packages on non-Intel processors.

Multi-core ready

Thread-Safety
All Intel MKL functions are thread-safe.  A non-threaded serial version of Intel MKL is also provided.

Automatic runtime processor detection
An automatic runtime check ensures that processor-specific optimized code is executed, and that your application achieves optimal performance on every system.

Support for C and Fortran interfaces
Unlike some alternative math libraries that require you to purchase multiple products to get C and Fortran interfaces, Intel MKL includes both.

Support for all Intel® processors in one package
Other math libraries require you to purchase multiple products for support of Intel Itanium 2, Intel Xeon, and Pentium 4 processors. Intel MKL includes support for ALL of these processors in a single, inexpensive package.

Royalty-free distribution rights
Redistribute unlimited copies of the runtime libraries with your software.

User forum
Share experiences with others at the Intel engineer-moderated Intel MKL Discussion Forum.

Intel® Premier Support
Receive one year of world-class technical support with every purchase of Intel MKL. During this period, you can download product upgrades free of charge, including major version releases. For more information, visit the Intel Registration Center.

Linear Algebra - BLAS and LAPACK
Deploy BLAS and LAPACK routines that are highly optimized for Intel processors, and provide significant performance improvements over alternative implementations. Intel MKL is compliant with the new 3.1 release of LAPACK.

Linear Algebra - ScaLAPACK
The Intel MKL implementation of ScaLAPACK can provide significant performance improvements over the standard NETLIB implementation.

Linear Algebra- Sparse Solvers
Solve large, sparse linear systems of equations with the PARDISO Direct Sparse Solver – an easy-to-use, thread-safe, high-performance, and memory-efficient software library licensed from the University of Basel. Intel MKL also includes Conjugate Gradient and FGMRES iterative sparse solvers.

Fast Fourier Transforms (FFT)
Employ multi-dimensional FFT routines (1D up to 7D) with mixed radix support and a modern, easy-to-use C/Fortran interface. Intel MKL also provides a set of C routines ("wrappers") that mimic the FFTW 2.x and 3.0 interfaces making it easy for current FFTW users to compare performance with Intel MKL.

Vector Math Library
Increase application speeds with vectorized implementations of computationally-intensive, core mathematical functions (power, trigonometric, exponential, hyperbolic, logarithmic, etc).

Vector Random Number Generators
Speed up your simulations using our vector random number generators, which can provide substantial performance improvements over scalar random number generator alternatives.

LINPACK Benchmark
Intel provides free LINPACK benchmark packages to help you obtain the highest possible benchmark results for your Intel® architecture-based systems.

In this release of Intel Math Kernel Library (Intel MKL 10.1), we have focused on three primary objectives.  First and always foremost is providing optimized multi-threaded performance for the newest Intel Processors (Quad-Core Intel Xeon processor 5300 series, its close relative the Dual-Core Intel® Xeon® processor 5100 series, and the Intel® Core™ i7).  Secondly, we have re-architected Intel MKL with a new “layered” architecture to better support the varied usage models of our users.  Lastly, we have merged the standard and cluster editions of Intel MKL into a single, comprehensive package.

Optimizations for the new Quad-Core Intel Xeon processor 5300 series and the Intel® Core™ i7
For more information, see section “Performance Improvements in Version 10.1” below.

New “Layered” Architecture
Intel MKL 10.0 introduced a re-architected product—providing multiple layers, and allowing the base Intel MKL package to support numerous configurations of interfaces, compilers, and processors in a single package.  Many other library vendors have specific versions that must first be found, downloaded, installed, and tested depending on the particular configuration of your development environment.  This new Intel MKL architecture is intended to provide maximum support for our varied customers’ needs, while minimizing the effort it takes to obtain and utilize the great performance of Intel MKL.  For more information, please refer to the “Using Intel MKL Parallelism” section of the Intel MKL User’s Guide.

PARDISO Direct Sparse Solver

Sparse BLAS

Routines for computing the sum and product of two sparse matrices stored in the compressed sparse row format.

Routines for converting between different  sparse matrix formats have been added.

Routines for converting between different  sparse matrix formats have been added.

Sparse 0-based indexing

Single precision support added

Level-3 Sparse BLAS triangular solvers were threaded

LAPACK

The capability to track and/or interrupt the progress of lengthy LAPACK computations has been added via a callback function mechanism. A function called mkl_progress can be defined in a user application, which will be called regularly from a subset of the MKL LAPACK routines. Refer to the specific function descriptions to see which LAPACK functions support the feature.

Discrete Fourier Transform Interface (DFTI)

The DftiCopyDescriptor function has been added for convenience when using the FFTs.

The size of statically linked executables calling DFTI has been reduced significantly.

Complex storage is now available for real-to-real transforms.

Iterative Solver Preconditioner

ILUT accelerator/preconditioner for the Intel MKL RCI iterative solvers

Vector Math Functions

New Mul, Conj, MulbyConj, CIS, Abs functions.

New “Enhanced Performance” mode. EP Mode is for applications where math function inaccuracies do not dominate parameter inaccuracies (e.g. Monte Carlo simulations and Media applications).

All VML functions are now threaded

Optimized versions of the Cumulative Normal Distribution (CdfNorm), its inverse (CdfNormInv), and the inverse complementary error function (ErfcInv) have been added to the Vector Math Library.

User’s Guide

We have greatly improved our Intel MKL User’s Guide.  It is an indispensable tool for working with Intel MKL. Visit the Documentation page to download or view it online.

Performance optimizations were done in all areas of the library. Below are some specific, measured performance gains. A list of performance improvements in past versions of Intel MKL is available on the Performance Improvements page. Performance charts are shown on each product domain page (BLAS/LAPACK, FFT, VML, etc).

BLAS

Direct  sparse solver (DSS/PARDISO):

35% performance improvement on average for out-of-core PARDISO

VML and VSL

Operating Systems
Intel MKL 10.1 supports Linux*, Windows*, and Mac OS* X.

Linux variants include: Red Hat*, Suse*, Debian*, Ubuntu*, Asianux*, and other Linux Standard Base 3.1 variants.

Every purchase of an Intel® Software Development Product includes a year of support services, which provides access to Intel Premier Support and all product updates during that time. Intel Premier Support gives you online access to technical notes, application notes, and documentation. Install the product, and then register to get support and product update information.