Biblioteca central de matemáticas Intel®

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
  • Avanzado
  • Principiante
  • Intermedio
  • Biblioteca central de matemáticas Intel®
  • Intel® Advanced Vector Extensions (Intel® AVX)
  • sgemm
  • DGEMM
  • Intel® AVX2
  • MKL PARDISO
  • 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:

    Using community license of Intel MKL for multiple users

    The community license is is a "Named-User" license.

    My understanding is that multiple copies of the library has to be installed for each end user. However, this proves to be a little technical difficulty for us. Security is a major concern for us. The server will be isolated from internet except for limited access for certain business activities. Each end user will not be able to transfer data in or out the server. Only the administrator can do it.

     

     

    英特尔和 Facebook* 共同协作,在英特尔 CPU 上提高 Caffe2 的性能

                               

    作者:Andres Rodriguez 和 Niveditha Sundaram

    每天,全球生成了越来越多的信息,包括文本、照片和视频中的信息。最近几年,借助最先进的语音识别、图像/视频识别和推荐引擎,人工智能和深度学习改善了多款应用,帮助人们更好地理解这些信息。

    MKL FFT 2D

    Hello,

    I am trying to compute the solution to the Laplace differential equation using a 2D FFT and MKL. 

    Are there sample codes to compute the solution to 2nd order differential equations using forward and inverse FFT?

    The issue appears to be when I multiply the transformed values by the wavenumbers.

    Thanks

    Suscribirse a Biblioteca central de matemáticas Intel®