Intel® Math Kernel Library

Intel® HPC Developer Conference 2017

Dear MKL Forum Users, join us at Intel® HPC Developer Conference in Denver, Colorado during November 11-12, 2017.  This free technical training is open to the public and will feature industry luminaries sharing best practice and techniques for maximizing efficiency and getting the benefits from Intel architecture. Attendees have the option of attending technical sessions, hands-on tutorials and poster sessions that cover parallel programming, high productivity languages, artificial intelligence, systems, enterprise, visualization development and more.     

Announcing new tool -- Intel® Math Kernel Library LAPACK Function Finding Advisor

The Intel® Math Kernel Library (Intel® MKL) LAPACK domain contains a huge variety of routines. Now, a new tool is provided with a faster method of finding appropriate LAPACK functions in Intel® Math Kernel Library Developer Reference document. This tool would be very useful for Intel® MKL newbies and for users not familiar with LAPACK function naming conventions. By using this tool, users can specify functionality as parameters in drop down lists, descriptions of all functions satisfying the requirements will be shown through this tool. 

Intel® MKL 11.3.3 patch

There are two listed below limitations with Intel® Math Kernel Library (Intel® MKL) 11.3 Update 3 which were discovered recently. The official fix of these issues will be available the nearest update Intel MKL 11.3.4.

If you require an immediate Intel MKL update to address these issues, please submit a ticket at Intel Premier Support ( for the Intel MKL product.

Known Limitations: 

  • FreeBSD*
  • Linux*
  • Microsoft Windows* (XP, Vista, 7)
  • Microsoft Windows* 10
  • Microsoft Windows* 8.x
  • Unix*
  • Fortran
  • Advanced
  • Beginner
  • Intermediate
  • Intel® Math Kernel Library
  • Intel® Advanced Vector Extensions (Intel® AVX)
  • sgemm
  • Intel® AVX2
  • SVD speed of 'small' matrices in MKL 2018_0_124

    I'm using SVD during some least-square fitting, typically operating on spectral data (1000-2000 data points) and fitting with very few parameters (2-5).

    For this, I'm generally using a direct implementaion of the SVD routines from the "numerical recipes" (single-threaded).

    When I started needing SVDs in other areas (bigger matrices with a less extreme aspect ratio, typtically ~ 10000 x 1000) I started using MKL Lapacke, currenlty using version 2017_4_210 and here the routines greatly outperform the NR routines.

    matlab no longer working after installing mkl


    I installed mkl_2018 on a linux Debian 8.0 64bits where I already had matlab installed. Before installing mkl, matlab worked fine.

    Since I installed mkl, matlab starts but crashes with following error when I do a signal convolution:

    Intel MKL FATAL ERROR: cannot load

    So I added 

    export LD_PRELOAD=/opt/intel/mkl/lib/intel64/ to my .bashrc and sourced it, and now I can no longer launch matlab : I get following error message:

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