英特尔® 数学核心函数库

Announcing new open source project Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN)

Intel® Math Kernel Library for Deep Neural Networks (Intel® MKL-DNN) is now available on the Github (https://github.com/01org/mkl-dnn) as an open source performance library for Deep Learning (DL) applications intended for acceleration of DL frameworks on Intel® architecture. Intel® MKL-DNN includes highly vectorized and threaded building blocks to implement convolutional neural networks (CNN) with C and C++ interfaces.

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 (https://premier.intel.com) for the Intel MKL product.

Known Limitations: 

  • FreeBSD*
  • Linux*
  • Microsoft Windows* (XP, Vista, 7)
  • Microsoft Windows* 10
  • Microsoft Windows* 8.x
  • Unix*
  • Fortran
  • 高级
  • 入门级
  • 中级
  • 英特尔® 数学核心函数库
  • 英特尔® 高级矢量扩展指令集
  • sgemm
  • DGEMM
  • Intel® AVX2
  • MKL PARDISO
  • MKL library

    Sir

    I have to install a code. it requiyes linking of lapack n blas file.
    the code was written in 2009 using mkl 8 version. according to it for linking paths are

    LROOT = /opt/intel/mkl/lib/intel64/
    LAPACK = -lmkl_lapack -lmkl
    BLAS = -L$(LROOT) -lmkl_intel64 -lguide -lpthread

    LFLAGS = $(LIBSCE) $(BLAS) $(LAPACK)

    now i am having 2016 version of mkl. it does not have guide, mkl, pthread etc.
    i know
    -lmkl_lapack is replaced by lmkl_lapack95_ilp64

    how to modify the commands as per 2016 version to link n compile

    thanks

    Segmentation faults with sparse FEAST

    Dear,

    I am using the interface dfeast_scsrev for computing eigenvalues and eigenvectors of a sparse matrix sorted using a CSR format (3-vector).

    It works fine with small sparse matrices with  a size of about ~10,000. However, I got a segmentation fault with a sparse matrix of size ~130,000 or bigger.

    Below is the error message I got:

    Are LAPACKE_cgesdd and LAPACKE_cgesvd SVD calculations reliable?

    I'm using LAPACKE_cgesdd and LAPACKE_cgesvd to compute the singular values of a matrix. Both the routines have the option to compute the singular values only. The problem I have is that, in the following four test cases:

    1. Full SVD with LAPACKE_cgesdd;
    2. Full SVD with LAPACKE_cgesvd;
    3. Singular values only with LAPACKE_cgesdd;
    4. Singular values only with LAPACKE_cgesvd.

    I receive different singular values. In particular:

    Test, 3 x 4 matrix

    "Trust Region Algorithm" Questions

    Along this period, we have developed a calculation method that uses the Trust Region MKL API (with constraints).

    We found many difficulties, but after a lot of efforts we have obtained some quite good results.

    By the way, we have found also some strange behavior of your functions (eg. dtrnlspbc_solve …).

    Here some question that can help us and also other users to understand the usage of this algorithm better:

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