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Intel® Platform Analysis Library Metrics Framework User Guide

Click "Download" below to obtain and view Intel® Platform Analysis Library Metrics Framework User Guide

Authored by Last updated on 06/23/2019 - 18:50
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Maximize TensorFlow* Performance on CPU: Considerations and Recommendations for Inference Workloads

This article will describe performance considerations for CPU inference using Intel® Optimization for TensorFlow*
Authored by Nathan Greeneltch (Intel) Last updated on 07/31/2019 - 12:11
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最大限度提升 CPU 上的 TensorFlow* 性能:推理工作负载的注意事项和建议

本文将介绍使用面向 TensorFlow 的英特尔® 优化* 进行 CPU 推理的性能注意事项
Authored by Nathan Greeneltch (Intel) Last updated on 08/09/2019 - 02:02
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Intel® Data Analytics Acceleration Library - Decision Trees

Decision trees method is one of most popular approaches in machine learning. They can easily be used to solve different classification and regression tasks.
Authored by Gennady F. (Blackbelt) Last updated on 09/17/2019 - 16:25
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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...
Authored by David S. (Blackbelt) Last updated on 10/15/2019 - 19:42
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Core Challenge In Speeding Up Python, PHP, HHVM, Node.js...

A traditional compiler translates a high-level computer program into machine code for the CPU you want to run it on. An interpreted language translates a high-level language into the machine code for some imaginary CPU. For historical reasons, this imaginary CPU is called a "virtual machine" and its instructions are called "byte code." One advantage of this approach is development speed: creating...
Authored by David S. (Blackbelt) Last updated on 10/15/2019 - 19:43