Introducing the Intel® Math Kernel Library

The Intel® Math Kernel Library (Intel® MKL) improves performance of scientific, engineering, and financial software that solves large computational problems. Among other functionality, Intel MKL provides linear algebra routines, fast Fourier transforms, as well as vectorized math and random number generation functions, all optimized for the latest Intel processors, including processors with multiple cores (see the Intel® MKL Release Notes for the full list of supported processors). Intel MKL also performs well on non-Intel processors.

Intel MKL is thread-safe and extensively threaded using the OpenMP* technology.

Intel MKL provides the following major functionality:

• Linear algebra, implemented in LAPACK (solvers and eigensolvers) plus level 1, 2, and 3 BLAS, offering the vector, vector-matrix, and matrix-matrix operations needed for complex mathematical software. If you prefer the FORTRAN 90/95 programming language, you can call LAPACK driver and computational subroutines through specially designed interfaces with reduced numbers of arguments. A C interface to LAPACK is also available.
• ScaLAPACK (SCAlable LAPACK) with its support functionality including the Basic Linear Algebra Communications Subprograms (BLACS) and the Parallel Basic Linear Algebra Subprograms (PBLAS). ScaLAPACK is available for Intel MKL for Linux* and Windows* operating systems.
• Direct sparse solver, an iterative sparse solver, and a supporting set of sparse BLAS (level 1, 2, and 3) for solving sparse systems of equations.
• Multidimensional discrete Fourier transforms (1D, 2D, 3D) with a mixed radix support (for sizes not limited to powers of 2). Distributed versions of these functions are provided for use on clusters on the Linux* and Windows* operating systems.
• A set of vectorized transcendental functions called the Vector Math Library (VML). For most of the supported processors, the Intel MKL VML functions offer greater performance than the libm (scalar) functions, while keeping the same high accuracy.
• The Vector Statistical Library (VSL), which offers high performance vectorized random number generators for several probability distributions, convolution and correlation routines, and summary statistics functions.
• Data Fitting Library, which provides capabilities for spline-based approximation of functions, derivatives and integrals of functions, and search.