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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.
Authored by Last updated on 07/06/2019 - 16:40
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面向英特尔® 至强融核™ 处理器的 Offload over Fabric教程

This tutorial shows how to install Offload over Fabric (OoF) software on 2nd generation Intel® Xeon Phi™ processor, configure the hardware, test the basic configuration, and enable OoF
Authored by Nguyen, Loc Q (Intel) Last updated on 03/21/2019 - 12:00
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整理您的数据和代码: 数据和布局 - 第 2 部分

Apply the concepts of parallelism and distributed memory computing to your code to improve software performance. This paper expands on concepts discussed in Part 1, to consider parallelism, both vectorization (single instruction multiple data SIMD) as well as shared memory parallelism (threading), and distributed memory computing.
Authored by David M. Last updated on 07/06/2019 - 16:40
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Set up Offload Over Fabric Software on an Intel® Xeon Phi™ Processor

How to install and enable Offload Over Fabric, configure the hardware, and test the configuration.
Authored by Nguyen, Loc Q (Intel) Last updated on 06/14/2019 - 11:50
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Code Sample: Optimizing Binarized Neural Networks on Intel® Xeon® Scalable Processors

In the previous article, we discussed the performance and accuracy of Binarized Neural Networks (BNN). We also introduced a BNN coded from scratch in the Wolfram Language. The key component of this neural network is Matrix Multiplication.
Authored by Yash Akhauri Last updated on 03/21/2019 - 12:40
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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
Authored by Nguyen, Loc Q (Intel) Last updated on 03/21/2019 - 12:00
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Improve Application Performance on an Intel® Xeon Phi™ Processor

Learn techniques for vectorizing code, adding thread-level parallelism, and enabling memory optimization.
Authored by Nguyen, Loc Q (Intel) Last updated on 06/14/2019 - 11:50
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借助 SIMD 数据布局模板优化数据布局

Financial service customers need to improve financial algorithmic performance for models such as Monte Carlo, Black-Scholes, and others. SIMD programming can speed up these workloads. In this paper, we perform data layout optimizations using two approaches on a Black-Scholes workload for European options valuation from the open source Quantlib library.
Authored by Nimisha R. (Intel) Last updated on 12/12/2018 - 18:00
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Putting Your Data and Code in Order: Data and layout - Part 2

Apply the concepts of parallelism and distributed memory computing to your code to improve software performance. This paper expands on concepts discussed in Part 1, to consider parallelism, both vectorization (single instruction multiple data SIMD) as well as shared memory parallelism (threading), and distributed memory computing.
Authored by David M. Last updated on 07/06/2019 - 16:40
Article

Performance of Classic Matrix Multiplication Algorithm on Intel® Xeon Phi™ Processor System

Matrix multiplication (MM) of two matrices is one of the most fundamental operations in linear algebra. The algorithm for MM is very simple, it could be easily implemented in any programming language. This paper shows that performance significantly improves when different optimization techniques are applied.
Authored by Last updated on 06/14/2019 - 11:50