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
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

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

Thread Parallelism in Cython*

Cython* is a superset of Python* that additionally supports C functions and C types on variable and class attributes. Cython generates C extension modules, which can be used by the main Python program using the import statement.
作者: Nguyen, Loc Q (Intel) 最后更新时间: 2019/07/06 - 16:30
Article

Intel® Math Kernel Library for Deep Neural Networks: Part 2 – Code Build and Walkthrough

Learn how to configure the Eclipse* IDE to build the C++ code sample, along with a code walkthrough based on the AlexNet deep learning topology for AI applications.
作者: Bryan B. (Intel) 最后更新时间: 2018/05/23 - 11:00
Article

英特尔® MKL-DNN:第二部分 – 代码示例创建与详解

在本篇中 (系列教程第二部分),将介绍如何配置集成开发环境 (IDE),以创建 C++ 代码示例,并提供基于 AlexNet* 深度学习拓扑的代码详解。
作者: Bryan B. (Intel) 最后更新时间: 2018/05/23 - 11:00
博客

Big Datasets from Small Experiments

作者: Andrey Vladimirov 最后更新时间: 2019/07/04 - 18:46
File Wrapper

Parallel Universe Magazine - Issue 28, April 2017

作者: 管理 最后更新时间: 2019/03/21 - 12:00
博客

Intel® CPU Excels in MLPerf* Reinforcement Learning Training

Today, MLPerf* consortium, a group of 40 companies and university research institutes, published the 2nd round of the benchmark results based upon ML

作者: Koichi Yamada (Intel) 最后更新时间: 2019/07/10 - 16:24