博客

Optimizing Big Data processing with Haswell 256-bit Integer SIMD instructions

Big Data requires processing huge amounts of data. Intel Advanced Vector Extensions 2 (aka AVX2) promoted most Intel AVX 128-bits integer SIMD instruction sets to 256-bits.

作者: gaston-hillar (Blackbelt) 最后更新时间: 2019/07/06 - 17:00
博客

The JITter Conundrum - Just in Time for Your Traffic Jam

In interpreted languages, it just takes longer to get stuff done - I earlier gave the example where the Python source code a = b + c would result in a BINARY_ADD byte code which takes 78 machine instructions to do the add, but it's a single native ADD instruction if run in compiled language like C or C++. How can we speed this up? Or as the performance expert would say, how do I decrease...
作者: David S. (Blackbelt) 最后更新时间: 2019/07/04 - 20:00
Article

Free access to Intel® Compilers, Performance libraries, Analysis tools and more...

Intel® Parallel Studio XE is a very popular product from Intel that includes the Intel® Compilers, Intel® Performance Libraries, tools for analysis, debugging and tuning, tools for MPI and the Intel® MPI Library. Did you know that some of these are available for free? Here is a guide to “what is available free” from the Intel Parallel Studio XE suites.
作者: 管理 最后更新时间: 2019/03/21 - 12:00
Article

Manage Deep Learning Networks with Caffe* Optimized for Intel® Architecture

How to optimize Caffe* for Intel® Architecture, train deep network models, and deploy networks.
作者: Andres Rodriguez (Intel) 最后更新时间: 2019/03/11 - 13:17
Article

Caffe* Optimized for Intel® Architecture: Applying Modern Code Techniques

This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
作者: 最后更新时间: 2019/07/06 - 16:40
Article

Intel® IPP ZLIB Coding Functions

1. Overview
作者: Chao Y (Intel) 最后更新时间: 2019/07/31 - 14:30
Article

Introducing DNN primitives in Intel® Math Kernel Library

Please notes: Deep Neural Network(DNN) component in MKL is deprecated since intel® MKL ​2019 and will be removed in the next intel® MKL Release.

作者: Vadim Pirogov (Intel) 最后更新时间: 2019/03/21 - 12:00
Article

Running Intel® Parallel Studio XE Analysis Tools on Clusters with Slurm* / srun

Since HPC applications target high performance, users are interested in analyzing the runtime performance of such applications.

作者: Michael Steyer (Intel) 最后更新时间: 2019/07/06 - 11:23
Article

面向英特尔® 架构优化的 Caffe*:使用现代代码技巧

This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
作者: 最后更新时间: 2019/07/06 - 16:40
Article

借助针对英特尔® 架构优化的 Caffe* 管理深度学习网络

如何面向英特尔® 架构优化 Caffe*,训练深度网络模型及部署网络。
作者: Andres Rodriguez (Intel) 最后更新时间: 2019/03/11 - 13:17