Software developers wanting to enjoy the performance benefits of Intel® oneAPI Math Kernel Library (oneMKL) in C++ environments can use popular open source C++ template libraries and link them with oneMKL. These higher-level libraries enable users to use abstracted C++ classes to perform vector math, BLAS, LAPACK, and some sparse computations while achieving performance similar to what the oneMKL library provides.
To discover more about available C++ libraries, see the documentation linked in the following table.
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C++ Math Library |
Supported oneMKL Functionality |
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BLAS (level 2, 3) LAPACK (LU factorization, Cholesky, QR factorization, singular value decomposition [SVD], Eigenvalues, Schur) VML PARDISO* |
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BLAS (DOT, GEMV, GEMM) LAPACK (LU factorization, Cholesky, QR factorization, SVD, Eigenvalues) |
|
|
BLAS LAPACK |
|
|
BLAS LAPACK |
Note Issues found in the C++ libraries listed in this table should be reported to the open source library owners. Any issues determined to be caused by oneMKL should be reported on the Developer Software Forums or Online Service Center.