Developer Reference for Intel® Math Kernel Library 2019 - C

Basic Linear Algebra Subprograms (BLAS)

The BLAS routines provide vector, matrix-vector, and matrix-matrix operations.

Sparse BLAS

The Sparse BLAS routines provide basic operations on sparse vectors and matrices.

Sparse QR

The Sparse QR routines provide a multifrontal sparse QR factorization method for solving a sparse system of linear equations.

LAPACK

The LAPACK routines solve systems of linear equations, least square problems, eigenvalue and singular value problems, and Sylvester's equations.

Intel® Math Kernel Library (Intel® MKL) functions for Deep Neural Networks (DNN functions)

Intel® MKL functions for Deep Neural Networks (DNN functions) is a collection of performance primitives for Deep Neural Networks (DNN) applications optimized for Intel® architecture. The implementation of DNN functions includes a limited set of primitives used in the AlexNet topology.

Direct and Iterative Sparse Solvers

Among several options for solving sparse linear systems of equations, Intel MKL offers a direct sparse solver based on PARDISO*, which is referred to here as Intel MKL PARDISO.

Vector Mathematics Functions

The Vector Mathematics (VM) functions compute core mathematical functions on vector arguments.

Vector Statistics Functions

The Vector Statistics (VS) functions generate vectors of pseudorandom numbers with different types of statistical distributions and perform convolution and correlation computations.

Fourier Transform Functions

The Fourier Transform Functions offer several options for computing Fast Fourier Transforms (FFTs).