Questions tagged [intel-mkl]

Intel MKL (Math Kernel Library) is a high performance math library specifically optimised for Intel processors. Its core functions include BLAS and LAPACK linear algebra routines, fast Fourier transforms and vector math functions amongst others.

Intel MKL (Math Kernel Library) is a high performance math library specifically optimised for Intel processors and explicitly parallelised with a version specifically available for High End Supercomputer clusters. Its core functions include BLAS and LAPACK linear algebra routines, fast Fourier transforms and vector math functions amongst others.

Intel MKL only supports Intel and compatible processors and is available for Windows, Linux and OS X as part of Intel® Parallel Studio and Intel® System Studio. There are free versions available for Students and Academic researchers at qualifying institutions.

The Intel® Math Kernel Library includes the following groups of routines:

  • Basic Linear Algebra Subprograms (BLAS):
    • vector operations
    • matrix-vector operations
    • matrix-matrix operations
  • Sparse BLAS Level 1, 2, and 3 (basic operations on sparse vectors and matrices)
  • LAPACK routines for solving systems of linear equations
  • LAPACK routines for solving least squares problems, eigenvalue and singular value problems, and Sylvester's equations
  • Auxiliary, utility, and test LAPACK routines
  • ScaLAPACK computational, driver and auxiliary routines (only in Intel MKL for Linux* and Windows* operating systems)
  • PBLAS routines for distributed vector, matrix-vector, and matrix-matrix operation
  • Direct and Iterative Sparse Solver routines, including a solver based on the PARDISO* sparse solver and the Intel MKL Parallel Direct Sparse Solver for Clusters
  • Direct Sparse Solver (DSS)
  • Extended Eigensolver routines for solving symmetric standard or generalized symmetric definite eigenvalue problems using the Feast algorithm
  • Vector Mathematical Library (VML) functions for computing core mathematical functions on vector arguments (with Fortran and C interfaces)
  • Vector Statistical Library (VSL) functions for generating vectors of pseudorandom numbers with different types of statistical distributions and for performing convolution and correlation computations
  • General Fast Fourier Transform (FFT) Functions, providing fast computation of Discrete Fourier Transform via the FFT algorithms and having Fortran and C interfaces
  • Cluster FFT functions (only in Intel MKL for Linux* and Windows* operating systems)
  • Tools for solving partial differential equations - trigonometric transform routines and Poisson solver
  • Optimization Solver routines for solving nonlinear least squares problems through the Trust-Region (TR) algorithms and computing Jacobi matrix by central differences
  • Basic Linear Algebra Communication Subprograms (BLACS) that are used to support a linear algebra oriented message passing interface
  • Data Fitting functions for spline-based approximation of functions, derivatives and integrals of functions, and search
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numpy matrix mult does not work when it is parallized on HPC

I have two dense matrices with the sizes (2500, 208) and (208, 2500). I want to calculate their product. It works fine and fast when it is a single process but when it is in a multiprocessing block, the processes stuck in there for hours. I do…
rando
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How to fix Octave producing wrong results with Intel MKL in Ubuntu?

Although Intel MKL speeds up calculations in GNU Octave, the results are sometimes (tested with Octave 5.2.0 in Xubuntu 20.04) totally wrong when the size of the Matrices are big. This has been mentioned here and here. For example, this gist shows…
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When you have an AMD CPU, can you speed up code that uses the Intel-MKL?

I have an AMD cpu and I'm trying to run some code that uses Intel-MKL. The code is significantly slower than I expected. When you have an AMD CPU, can you speed up code that uses the Intel-MKL? How?
Trevor Boyd Smith
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Python, Intel Python and multi-core processors

I recently carried out a couple of tests on a Linux server with 2 processors each featuring 20 physical cores (full hardware description of one processor is given below) along with 20 additional logical cores (thus 80 cores in total). The reason for…
Alain
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Will `mkl_set_num_threads` upper-bound to the number of CPU Threads?

In OpenBLAS, if you call openblas_set_num_threads asking for a number of threads which is to be higher than the number of CPU threads that you have, then the actual number of threads it will be set to use is your number of CPU Threads. This can be…
Frames Catherine White
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Fortran double precision program with a simple MKL BLAS routine

In trying to mix precision in a simple program - using both real and double - and use the ddot routine from BLAS, I'm coming up with incorrect output for the double precision piece. Here's the code: program test !! adding this statement narrowed…
Shamster
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Configuring MKL with R

I'm having a problem to trigger threads with R using the MKL library. I am currently using Ubuntu 18.04.2 LTS. Linux pedro-HP-EliteOne-800-G1-AiO 4.18.0-15-generic #16~18.04.1-Ubuntu SMP Thu Feb 7 14:06:04 UTC 2019 x86_64 x86_64 x86_64…
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Why does Tensorflow warn about AVX2 while I am using MKL?

I am using Tensorflow's Anaconda distribution with MKL support. from tensorflow.python.framework import test_util test_util.IsMklEnabled() This code prints True. However, when I compile my Keras model I still get Your CPU supports instructions…
ozgur
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What optimizations are shipped in Intel Distribution for Python?

Intel strongly recommends using their distribution for Python, instead of manually building Python modules for yourself. An obvious advantage is that there are many optimized modules available from their distribution, a non-trivial task if you want…
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How does LLVM translate OpenMP multi-threaded code with runtime library calls?

When I studied the LLVM OpenMP Runtime Library document, I found there is an example about work sharing: extern float foo( void ); int main () { int i; float r = 0.0; #pragma omp parallel for schedule(dynamic) reduction(+:r) for ( i…
Yu-Wen Lai
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Multiple mkl packages installed in anaconda

I've just observed that there are tree different version of mkl package installed on my computer. du -sh */ 417M mkl-2017.0.1-0/ 407M mkl-2017.0.3-0/ 557M mkl-2018.0.1-hfbd8650_4/ 526M mkl-2018.0.2-1/ I know that mkl package included…
Daniel Chepenko
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CMake: How to use LINK_INTERFACE_MULTIPLICITY?

Linking against the Intel MKL static libraries introduces circular dependencies. When I import the libraries, set(LIBRARIES mkl_intel_lp64 mkl_sequential mkl_core) foreach(_lib ${LIBRARIES}) add_library(${_lib} UNKNOWN IMPORTED) …
Raul Laasner
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Keras with Tensorflow backend on GPU. MKL ERROR: Parameter 4 was incorrect on entry to DLASCL

I installed Tensorflow with GPU support and Keras to an environment in Anaconda (v1.6.5) by using following commands: conda install -n EnvName tensorflow-gpu conda install -n EnvName -c conda-forge keras-gpu I have NVIDIA Quadro 2200K on my machine…
Oleg O
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Linking LAPACK from Intel MKL with gfortran

I have a problem to link lapack to fortran example program. Here is the program example.f95 Program LinearEquations ! solving the matrix equation A*x=b using LAPACK Implicit none ! declarations double precision :: A(3,3), b(3) integer :: i,…
Vlada
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using mkl, error while loading shared libraries: libmkl_intel_lp64.so

I'm almost new in using mkl libraries. So excuse me if it seems silly. I tried to run an example in tutorial [here] with ifort -mkl dgemm_example.f ,then run the executable file. Here is the error: ./a.out: error while loading shared libraries:…
Abolfazl
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