Optimized Library for Scientific Computing

• Enhanced math routines enable developers and data scientists to create performant science, engineering, or financial applications
• Core functions include BLAS, LAPACK, sparse solvers, fast Fourier transforms (FFT), random number generator functions (RNG), summary statistics, data fitting, and vector math
• Optimizes applications for current and future generations of Intel® CPUs, GPUs, and other accelerators
• Is a seamless upgrade for previous users of the Intel® Math Kernel Library (Intel® MKL)

What’s New

• Data Parallel C++ (DPC++) APIs maximize performance and cross-architecture portability
• Introduces C and Fortran OpenMP offload for Intel® GPU acceleration

Learn about Data Parallel C++

What You Need

• The Intel® oneAPI Math Kernel Library (oneMKL) is available as part of the Intel® oneAPI Base Toolkit.
• An OpenMP* version is available as part of the Intel® oneAPI HPC Toolkit, including a cluster-accelerated oneMKL version for the Intel® MPI Library.

Explore the Intel® oneAPI Base Toolkit

Explore the Intel® oneAPI HPC Toolkit

Linear Algebra
Speed up linear algebra computations with low-level routines that operate on vectors and matrices, and are compatible with these industry-standard BLAS and LAPACK operations:

• Level 1: Vector-vector operations
• Level 2: Matrix-vector operations
• Level 3: Matrix-matrix operations

Sparse Linear Algebra Functions

• Sparse Matrix: dense vector multiplication
• Sparse Matrix: dense matrix multiplication
• Sparse triangular matrix solver

Fast Fourier Transforms (FFT)
Transform a signal from its original domain (typically time or space) into a representation in the frequency domain and back. Use FFT functions in one, two, or three dimensions with support for mixed radices. The supported functions include complex-to-complex and real-to-complex transforms of arbitrary length in single-precision and double-precision.

Random Number Generator Functions (RNG)
Use common pseudorandom, quasi-random, and non-deterministic random number engines to solve continuous and discrete distributions.

Vector Math
Balance accuracy and performance with vector-based elementary functions. Manipulate values with traditional algebraic and trigonometric functions.

Summary Statistics 
Compute basic statistical estimates (such as raw or central sums and moments) for single- and double-precision multidimensional datasets.

Get Started

• Intel oneAPI Math Kernel Library
• Podcast: Port Math Libraries Across Heterogeneous Architectures
• Webinar: Develop in a Heterogeneous Environment with Intel® oneAPI Math Kernel Library
• Webinar: Solve Enhanced Math Problems on GPUs: Linear Algebra, Sparse Matrices, and RNGs

Documentation

• Developer References
  DPC++ | C | Fortran
• Release Notes
• System Requirements
• License FAQ

View All Documentation

Code Samples

• Matrix Multiplication with CPUs and GPUs

View All Code Samples

Processors:

• Intel Atom® processors
• Intel® Core™ processors
• Intel® Xeon® Scalable processors

GPUs:

• Intel® Processor Graphics Gen9 and above
• X^e architecture

Languages:

• Data Parallel C++ (DPC++)
  
• C and C++
• C#
• Fortran*

Operating systems:

• Windows*
• Linux*
• macOS*

Compilers:

• Intel® oneAPI DPC++/C++ Compiler
• GNU Compiler Collection (GCC)*
• Intel® C++ Compiler Classic
• Intel® Fortran Compiler (Beta)
• Intel® Fortran Compiler Classic
• Other compilers that follow the same standards

Development environments:

• Windows: Microsoft Visual Studio*
• Linux: Eclipse* and Eclipse CDT (C/C++ Development Tooling)*

Threading models:

• Intel® oneAPI Threading Building Blocks
• OpenMP

For more information, see the system requirements.