Intel® Platform Analysis Library Metrics Framework User Guide

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作者: 最后更新时间: 2019/06/23 - 18:50

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*
作者: Nathan Greeneltch (Intel) 最后更新时间: 2019/07/31 - 12:11

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
作者: Nathan Greeneltch (Intel) 最后更新时间: 2019/08/09 - 02:02

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.
作者: Gennady F. (Blackbelt) 最后更新时间: 2019/09/17 - 16:25

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...
作者: David S. (Blackbelt) 最后更新时间: 2019/10/15 - 19:42

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...
作者: David S. (Blackbelt) 最后更新时间: 2019/10/15 - 19:43