腾讯* 在基于英特尔® 至强® 处理器的游戏内购买推荐系统中使用机器学习

To enhance the online gaming user experience, Tencent uses an in-game purchase recommendation system employing the machine learning method to help users decide what equipment they would want to buy within their games. Tencent machine learning engine uses DGEMM6 extensively in its module to compute the coefficients for the logistic regression machine learning algorithm.
作者: Nguyen, Khang T 最后更新时间: 2017/01/17 - 23:39

CPU 的性能超越 GPU,获京都大学青睐

The Kyoto University team recognized that the performance of the open source Theano C++ multi-core code could be significantly improved. They worked with Intel to improve Theano multicore performance using a dual-socket Intel® Xeon®processor based system as the next generation Intel® Xeon Phi™ processors were not available at that time
作者: 管理 最后更新时间: 2016/12/25 - 22:14

基于英特尔® 至强™ 处理器 E5 产品家族的多节点分布式内存系统上的 Caffe* 培训

Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and one of the most popular community frameworks for image recognition. Caffe is often used as a benchmark together with AlexNet*, a neural network topology for image recognition, and ImageNet*, a database of labeled images.
作者: Gennady F. (Intel) 最后更新时间: 2016/12/26 - 02:04

英特尔® 至强融核™ 处理器针对深度学习提供了出色的性能 - 正在迅速完善性能

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.
作者: Andres R. (Intel) 最后更新时间: 2016/12/26 - 00:19

R 语言中的OpenBLAS*和英特尔® 数学核心函数库的性能比较

Today, scientific and business industries collect large amounts of data, analyze them, and make decisions based on the outcome of the analysis. This paper compares the performance of Basic Linear Algebra Subprograms (BLAS), libraries OpenBLAS, and the Intel® Math Kernel Library (Intel® MKL).
作者: Nguyen, Khang T 最后更新时间: 2016/12/22 - 00:48

基于英特尔® 至强 E5 系列处理器的单节点 Caffe 评分和训练

As Deep Neural Network (DNN) applications grow in importance in various areas including internet search engines and medical imaging, Intel teams are working on software solutions to accelerate these workloads that will become available in future versions of Intel® Math Kernel Library (Intel® MKL) and Intel® Data Analytics Acceleration Library (Intel® DAAL). This technical preview demonstrates...
作者: Gennady F. (Intel) 最后更新时间: 2016/12/05 - 16:48

借助 SIMD 数据布局模板和数据预处理提高 SIMD 在动画中的使用效率

In this paper, we walk through a 3D Animation algorithm example and describe some techniques and methodologies that may benefit your next vectorization endeavors. We also integrate the algorithm with SIMD Data Layout Templates (SDLT), which is a feature of Intel® C++ Compiler, to improve data layout and SIMD efficiency. Includes code sample.
作者: Anson Chu (Intel) 最后更新时间: 2016/12/05 - 21:22

英特尔® 至强融核™ 处理器优化教程

In this tutorial, we demonstrate some possible ways to optimize an application to run on the Intel® Xeon Phi™ processor
作者: Nguyen, Loc Q 最后更新时间: 2016/11/14 - 00:39
Forum topic

vtune 支持 golang的一系列问题


作者: 玉锡 赵. 最后更新时间: 2016/08/24 - 07:51

英特尔® 至强™ 处理器 E7 v3 助力提升 Java* 应用性能


作者: Nguyen, Khang T 最后更新时间: 2016/08/17 - 14:22