Intel® Math Kernel Library

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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
  • Advanced
  • Beginner
  • Intermediate
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  • Intel® Advanced Vector Extensions
  • sgemm
  • DGEMM
  • Intel® AVX2
  • MKL PARDISO
  • Intel® Math Kernel Library 11.3 Update 4 is now available

    Intel® Math Kernel Library 11.3 Update 4 is now available

    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.

    Deep Neural Network extensions for Intel MKL

        Deep neural network (DNN) applications grow in importance in various areas including internet search engines, retail and medical imaging. Intel recognizes importance of these workloads and is developing software solutions to accelerate these applications on Intel Architecture that will become available in future versions of Intel® Math Kernel Library (Intel® MKL) and Intel® Data Analytics Acceleration Library (Intel® DAAL).

    While we are working on new functionality we published a series of articles demonstrating DNN optimizations with Caffe framework and AlexNet topology:

    Calling Python Developers - High performance Python powered by Intel MKL is here!

    We are introducing a Technical Preview of Intel® Distribution of Python*, with packages such as NumPy* and SciPy* accelerated using Intel MKL. Python developers can now enjoy much improved performance of many mathematical and linear algebra functions, with up to ~100x speedup in some cases, comparing to the vanilla Python distributions. The technical preview is available for everybody at no cost. Click here to register and download.

    Getting Started with Intel® Software Optimization for Theano* and Intel® Distribution for Python*

    Theano* is a Python* library developed at the LISA lab to define, optimize, and evaluate mathematical expressions, including the ones with multi-dimensional arrays. Theano can be installed and used with several combinations of development tools and libraries on a variety of platforms. This tutorial provides one such recipe describing steps to build and install Intel optimized-Theano with Intel® compilers and Intel MKL 2017 on CentOS* and Ubuntu* based systems.
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  • Linux*
  • Machine Learning
  • Python*
  • Intermediate
  • Intel® Parallel Studio XE
  • Intel® Math Kernel Library
  • Theano*
  • Intel® Advanced Vector Extensions
  • Academic
  • Big Data
  • Vectorization
  • Intel® Xeon Phi™ Delivers Competitive Performance For Deep Learning—And Getting Better Fast

    Baidu’s recently announced deep learning benchmark, DeepBench, documents performance for the lowest-level compute and communication primitives for deep learning (DL) applications. The goal is to provide a standard benchmark to evaluate different hardware platforms using the vendor’s DL libraries.
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