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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
  • Avanzado
  • Principiante
  • Intermedio
  • Biblioteca central de matemáticas Intel®
  • Intel® Advanced Vector Extensions (Intel® AVX)
  • sgemm
  • Intel® AVX2
  • AVX512 slower than AVX2? What I am doing wrong?

    Hello All,


    I was so excited to test new the new Intel Xeon Silver 4114 CPU just to find out that with AVX512 enabled the performance of the matrix multiplication is the same as with legacy SSE4. If I restrict the MKL library to use AVX2 only,  then the speed of the computation is twice as fast. What I am doing wrong here? The library seem to respond OK to the following call (here in FORTRAN):


    stat=mkl_cbwr_set (MKL_CBWR_AVX512)


    How to force AVX-2 vs AVX-512


    I'm running benchmarks of my code on test hardware (Intel Xeon Gold 5115), and i’m trying to isolate the impact of avx-512 vs avx-2 instructions on overall runtime. My issue is, I don’t know whether or not I’m forcing my code (compiled with icc 2018.1.163 + MKL) to use either instruction set. For reference (I can’t paste our entire codeset here, too long), the code is linear algebra heavy, and has used Intel MKL libraries via gsl_cblas_* calls, where GSL is also compiled with icc+MKL.

    Here’s the build scenario:

    Calling dgetrf_ before fork() causes HANG with MKL_CBWR=COMPATIBLE


    I have a mwe with the bug.  I reproduced with the following setup:


    export MKL_VERBOSE=1



    Foo is called
    MKL_VERBOSE Intel(R) MKL 2018.0 Update 1 Product build 20171007 for Intel(R) 64 architecture Intel(R) Architecture processors, Lnx 3.20GHz lp64 intel_thread NMICDev:0
    MKL_VERBOSE DGETRF(27,27,0x1f34280,27,0x7ffd36f28c20,27) 22.90ms CNR:COMPATIBLE Dyn:1 FastMM:1 TID:0  NThr:6 WDiv:HOST:+0.000
    Calling MPI_Init:

    foo compiled with:

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