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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Can't compile package with intel MKL ScaLapack and Blacs

I am trying to compile python package https://github.com/jrs65/scalapy which wraps mkl scalapack and blacs libraries. Compilation is successful however when I try to execute test programs I get: Intel MKL FATAL ERROR: Cannot load symbol…
Baranas
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Intel MKL FATAL ERROR: Cannot load libmkl_core.so

I'm trying to use train_test_split in Julia 1.0.5 with this code: using ScikitLearn using ScikitLearn.CrossValidation: train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2019) And I got this…
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MKL library error when using Neanderthal in Clojure

I'm trying to use the Neanderthal library in Clojure but I keep getting an error when executing any rand-normal! and rand-uniform!. I've installed the Intel MKL library, executed the mklvars.bat as mklvars intel64 file to set the environment…
Vasco Ferreira
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How can I make numpy use SSE4_2 instead of AVX?

I am struggling with a python reproducibility problem across different machines. I have two machines: an older server with SSE4_2 and a newer desktop with AVX. Running the same python script on both machines results in different outputs (I made sure…
ej_01
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How can I accelerate inference speed in TensorFlow when I got sparse matrix from pruning?

I got a sparse weight matrix from Tensorflow-pruning to reduce SqueezeNet. After strip_pruning_vars, I checked the most of elements in weight matrix pruned to 0 successfully. However, the performance of the model didn't increase on what I expected.…
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Weird FFT plot with numpy random set

Code below: import numpy as np from numpy import random_intel import mkl_fft import matplotlib.pyplot as plt n = 10**5 a = np.random_intel.rand(n) b = mkl_fft.fft(a) plt.scatter(b.real,b.imag) plt.show() print(b) for i in b : if i.real > n/2: …
Tiny tiny
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Causes of floating point non-determinism? Including NumPy?

IEEE floating point operations are deterministic, but see How can floating point calculations be made deterministic? for one way that an overall floating point computation can be non-deterministic: ... parallel computations are non-deterministic in…
Jerry101
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What value of alignment should I with mkl_malloc?

The function mkl_malloc is similar to malloc but has an extra alignment argument. Here's the prototype: void* mkl_malloc (size_t alloc_size, int alignment); I've noticed different performances with different values of alignment. Apart from trial…
avgn
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Conda forcing numpy and scipy to downgrade from openblas to non-working mkl version

Recently whenever I try and install something through conda I get the following: Fetching package metadata ............... Solving package specifications: . Package plan for installation in environment /Users/askates/anaconda3/envs/fastai: The…
as646
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python "Intel MKL FATAL ERROR: Cannot load libmkl_rt.so"

I am running multiple python ML jobs at same time. Most of the time my python script runs fine but when there are too many jobs starting at the same time, it fails with this error. It is also hard to reproduce this error as I am unable to find the…
Anchika Agarwal
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Calling BLAS / LAPACK directly using the SciPy interface and Cython

There was a post on this here: https://gist.github.com/JonathanRaiman/f2ce5331750da7b2d4e9 which shows a great speed improvement by just calling the Fortran libraries (BLAS / LAPACK / Intel MKL / OpenBLAS / whatever you installed with NumPy). After…
Matt
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Caffe throws mkl error while importing using mod_wsgi and httpd

I have a flask app which uses caffe compiled with mkl. The app is working fine when running using flask internal server or gunicorn. But when i try to run it using mod_wsgi and httpd, it throws the following error: Intel MKL FATAL ERROR: Cannot load…
Nefarious
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Linking Ipopt with Intel MKL

I'm trying to link Ipopt with Intel MKL (instructions). Intel's Link Advisor suggests: Link line: -Wl,--start-group ${MKLROOT}/lib/intel64/libmkl_intel_ilp64.a ${MKLROOT}/lib/intel64/libmkl_core.a ${MKLROOT}/lib/intel64/libmkl_intel_thread.a…
Agrim Pathak
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SciPy compatibility issue with MKL libraries

I recently installed SciPy for Python3.4 on Ubuntu 12.04 with pip3 install scipy When I import scipy in Python3.4 as: import scipy it imports, but when I import the function interpolate as import scipy.interpolate I get the following error: …
Tom Kurushingal
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Peculiar difference in MKL matrix multiplication performance between Fortran/Python/MATLAB

I've written a trivial benchmark comparing matrix multiplication performance in three languages - Fortran (using Intel Parallel Studio 2015, compiling with the ifort switches: /O3 /Qopt-prefetch=2 /Qopt-matmul /Qmkl:parallel, this replaces MatMul…