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Intel oneAPI Math Kernel Library (Intel oneMKL) , formerly known as Intel Math Kernel Library, is a library of optimized math routines for science, engineering, and financial applications. Core math functions include BLAS , LAPACK , ScaLAPACK , sparse solvers, fast Fourier transforms , and vector math.
Eigen is a vector mathematics library with performance comparable with Intel's Math Kernel Library; Hermes Project: C++/Python library for rapid prototyping of space- and space-time adaptive hp-FEM solvers. IML++ is a C++ library for solving linear systems of equations, capable of dealing with dense, sparse, and distributed matrices.
HP's Math library supporting IA-64, PA-RISC, x86 and Opteron architecture under HP-UX and Linux. Intel MKL The Intel Math Kernel Library, supporting x86 32-bits and 64-bits, available free from Intel. [7] Includes optimizations for Intel Pentium, Core and Intel Xeon CPUs and Intel Xeon Phi; support for Linux, Windows and macOS. [37] MathKeisan
Library Name Short. Name. Description; oneAPI DPC++ Library oneDPL Algorithms and functions to speed DPC++ kernel programming oneAPI Math Kernel Library: oneMKL Math routines including matrix algebra, FFT, and vector math oneAPI Data Analytics Library: oneDAL Machine learning and data analytics functions oneAPI Deep Neural Network Library oneDNN
Along with the compiler, AMD offers the AMD Optimizing CPU Libraries (AOCL), a set of numerical libraries that is roughly similar to Intel's Math Kernel Library and includes AMD Math Library (LibM), AMD Random Number Generator Library, AMD Secure RNG Library, AOCL-Sparse, BLIS, FFTW, libFLAME, and ScaLAPACK that are tuned for the Zen processors.
Intel's parallel libraries run on AMD hardware with the same performance and are fully supported by Intel. According to Werner Krotz-Vogel, Technical Marketing Engineer at Intel's Cluster Ready team, the Math Kernel Library (MKL) runs even faster than the open source library Automatically Tuned Linear Algebra Software (ATLAS) on an AMD Opteron ...
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BLIS also works on processors for which custom kernels have not yet been written; in those cases, the framework relies upon portable kernel implementations that perform at a lower rate of computation. BLIS is sometimes described as a refactoring of GotoBLAS2, which was created by Kazushige Goto at the Texas Advanced Computing Center. [9]