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Article

Caffe* Training on Multi-node Distributed-memory Systems Based on Intel® Xeon® Processor E5 Family

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.
Автор: Gennady F. (Blackbelt) Последнее обновление: 05.07.2019 - 14:54
Article

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.
Автор: Gennady F. (Blackbelt) Последнее обновление: 05.07.2019 - 14:55
Article

Scale-Up Implementation of a Transportation Network Using Ant Colony Optimization (ACO)

In this article an OpenMP* based implementation of the Ant Colony Optimization algorithm was analyzed for bottlenecks with Intel® VTune™ Amplifier XE 2016 together with improvements using hybrid MPI-OpenMP and Intel® Threading Building Blocks were introduced to achieve efficient scaling across a four-socket Intel® Xeon® processor E7-8890 v4 processor-based system.
Автор: Sunny G. (Intel) Последнее обновление: 05.07.2019 - 19:10
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.
Автор: Последнее обновление: 06.07.2019 - 16:40
Article

Tencent* Uses Machine Learning for In-Game Purchase Recommendation System on Intel® Xeon® Processors

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.
Автор: Nguyen, Khang T (Intel) Последнее обновление: 09.05.2019 - 13:08
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.
Автор: Последнее обновление: 06.07.2019 - 16:40
Article

应用蚁群优化算法 (ACO) 实施交通网络扩展

In this article an OpenMP* based implementation of the Ant Colony Optimization algorithm was analyzed for bottlenecks with Intel® VTune™ Amplifier XE 2016 together with improvements using hybrid MPI-OpenMP and Intel® Threading Building Blocks were introduced to achieve efficient scaling across a four-socket Intel® Xeon® processor E7-8890 v4 processor-based system.
Автор: Sunny G. (Intel) Последнее обновление: 05.07.2019 - 19:13
Article

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.
Автор: Nguyen, Khang T (Intel) Последнее обновление: 12.12.2018 - 18:00
Article

Using Intel® MPI Library on Intel® Xeon Phi™ Product Family

This document is designed to help users get started writing code and running MPI applications using the Intel® MPI Library on a development platform that includes the Intel® Xeon Phi™ processor.
Автор: Nguyen, Loc Q (Intel) Последнее обновление: 21.03.2019 - 12:00
Блоги

Track Reconstruction with Deep Learning at the CERN CMS Experiment

Connecting the Dots
Автор: Последнее обновление: 12.12.2018 - 18:00