Intel® Math Kernel Library (Intel® MKL) is a computing math library of highly optimized, extensively threaded routines for applications that require maximum performance. The library provides Fortran and C programming language interfaces. Intel MKL C language interfaces can be called from applications written in either C or C++, as well as in any other language that can reference a C interface.
Intel MKL provides comprehensive functionality support in these major areas of computation:
- BLAS (level 1, 2, and 3) and LAPACK linear algebra routines, offering vector, vector-matrix, and matrix-matrix operations.
- ScaLAPACK distributed processing linear algebra routines, as well as the Basic Linear Algebra Communications Subprograms (BLACS) and the Parallel Basic Linear Algebra Subprograms (PBLAS).
- Intel MKL PARDISO (a direct sparse solver based on Parallel Direct Sparse Solver PARDISO*), an iterative sparse solver, and supporting sparse BLAS (level 1, 2, and 3) routines for solving sparse systems of equations, as well as a distributed version of Intel MKL PARDISO solver provided for use on clusters.
- Fast Fourier transform (FFT) functions in one, two, or three dimensions with support for mixed radices (not limited to sizes that are powers of 2), as well as distributed versions of these functions provided for use on clusters.
- Vector Mathematics (VM) routines for optimized mathematical operations on vectors.
- Vector Statistics (VS) routines, which offer high-performance vectorized random number generators (RNG) 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.
- Extended Eigensolver, a shared memory programming (SMP) version of an eigensolver based on the Feast Eigenvalue Solver.
Intel MKL is 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.
For Windows* and Linux* systems based on Intel® 64 Architecture, Intel MKL also includes support for the Intel® Many Integrated Core (Intel® MIC) Architecture and provides libraries to help you port your applications to Intel MIC Architecture.
Benchmark your cluster with Intel® Optimized MP LINPACK Benchmark for Clusters or Intel® Optimized High Performance Conjugate Gradient Benchmark.
- Obtaining Numerically Reproducible Results.
It is your responsibility when using Intel MKL to ensure that input data has the required format and does not contain invalid characters. These can cause unexpected behavior of the library.
The library requires subroutine and function parameters to be valid before being passed. While some Intel MKL routines do limited checking of parameter errors, your application should check for NULL pointers, for example.
Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.
Notice revision #20110804