Intel® Math Kernel Library (Intel® MKL) is a highly optimized, extensively threaded, and thread-safe library of mathematical functions for engineering, scientific, and financial applications that require maximum performance.
Intel® MKL 2018 Beta is now available as part of the Parallel Studio XE 2018 Beta.
Check the Join the Intel® Parallel Studio XE 2018 Beta program post to learn how to join the Beta program, and the provide your feedback.
What's New in Intel® MKL 2018 Beta:
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.
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.
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.
-lmkl_lapack is replaced by lmkl_lapack95_ilp64
how to modify the commands as per 2016 version to link n compile
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:
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:
- Full SVD with LAPACKE_cgesdd;
- Full SVD with LAPACKE_cgesvd;
- Singular values only with LAPACKE_cgesdd;
- Singular values only with LAPACKE_cgesvd.
I receive different singular values. In particular:
Test, 3 x 4 matrix
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:
I have a question regarding the libmkl_blacs_openmpi* libraries. Which Openmpi version is this library supposed to be compatible with ?