Intel® Math Kernel Library (Intel® MKL) 2019 Beta Release Notes


This document provides a general summary of new features and important notes about the Intel® Math Kernel Library (Intel® MKL) software product.

Please see the following links to the online resources and documents for the latest information regarding Intel MKL:

Links to documentation, help, and code samples can be found on the main Intel MKL product page. For technical support visit the Intel MKL technical support forum and review the articles in the Intel MKL knowledge base.

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What’s New in Intel® Math Kernel Library (Intel® MKL) version 2019 beta

  • BLAS Features:
    • Introduced automatic S/DGEMM JIT capability for small matrix sizes (m,n,k <=16) to improve S/DGEMM performance for Intel® Advanced Vector Extensions 2 (Intel® AVX2)  and Intel® Advanced Vector Extensions 512 (Intel® AVX-512)  when compiling with MKL_DIRECT_CALL_JIT (threaded usage) or MKL_DIRECT_CALL_SEQ_JIT (sequential usage).
    • Introduced new functions to JIT (create) optimized S/DGEMM-like matrix multiply kernels for small matrix sizes (m,n,k <=16) for Intel® Advanced Vector Extensions 2 (Intel® AVX2)  and Intel® Advanced Vector Extensions 512 (Intel® AVX-512), execute the optimized kernel created using matrices with matching dimensions, and to remove (destroy) the JIT kernel.
  • FFT:
    • Improved performance of 1D real-to-complex FFT 
  • Sparse Solvers:
    • Introduce SparseQR functionality
    • Introduced Extreme{EVD/SVD} functionality
  • Random Generators
    • Introduced Multinominal Random Number Generators

Product Content

Intel MKL can be installed as a part of the following suite:

Intel MKL consists in one package for both IA-32 and Intel® 64 architectures and in online installer

Known Issues

  • Convolution primitives for forward pass may return incorrect results or crashes for the case where input spatial dimensions smaller than kernel spatial dimensions for Intel® Advanced Vector Extensions 512 (Intel® AVX-512)
  • Intel® MKL FFT – complex-to-complex in-place batched 1D FFT with transposed output returns incorrect output
  • Intel® ScaLAPACK may fail with OpenMPI* 1.6.1 and later releases due to known OpenMPI* issue: As a workaround, please avoid using OpenMPI
  • Intel® VML functions may raise spurious FP exceptions even if the (default) ML_ERRMODE_EXCEPT is not set. Recommendation: do not unmask FP exceptions before calling VML functions.
  • When an application uses Vector Math functions with the single dynamic library (SDL) interface combined with TBB threading layer, the application may generate runtime error “Intel MKL FATAL ERROR: Error on loading function mkl_vml_serv_threader_c_1i_2o.”

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The original versions of the BLAS from which that part of Intel MKL was derived can be obtained from

The original versions of LAPACK from which that part of Intel MKL was derived can be obtained from The authors of LAPACK are E. Anderson, Z. Bai, C. Bischof, S. Blackford, J. Demmel, J. Dongarra, J. Du Croz, A. Greenbaum, S. Hammarling, A. McKenney, and D. Sorensen. Our FORTRAN 90/95 interfaces to LAPACK are similar to those in the LAPACK95 package at All interfaces are provided for pure procedures.

The original versions of ScaLAPACK from which that part of Intel MKL was derived can be obtained from The authors of ScaLAPACK are L. S. Blackford, J. Choi, A. Cleary, E. D'Azevedo, J. Demmel, I. Dhillon, J. Dongarra, S. Hammarling, G. Henry, A. Petitet, K. Stanley, D. Walker, and R. C. Whaley.

The Intel MKL Extended Eigensolver functionality is based on the Feast Eigenvalue Solver 2.0

PARDISO (PARallel DIrect SOlver)* in Intel MKL was originally developed by the Department of Computer Science at the University of Basel . It can be obtained at

Some FFT functions in this release of Intel MKL have been generated by the SPIRAL software generation system ( under license from Carnegie Mellon University. The Authors of SPIRAL are Markus Puschel, Jose Moura, Jeremy Johnson, David Padua, Manuela Veloso, Bryan Singer, Jianxin Xiong, Franz Franchetti, Aca Gacic, Yevgen Voronenko, Kang Chen, Robert W. Johnson, and Nick Rizzolo.

For more complete information about compiler optimizations, see our Optimization Notice.