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Threading Intel® Integrated Performance Primitives Image Resize with Intel® Threading Building Blocks

Threading Intel® IPP Image Resize with Intel® TBB.pdf (157.18 KB) :
Authored by Jeffrey M. (Intel) Last updated on 07/31/2019 - 15:05
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Set Up Intel® Software Optimization for Theano* and Supporting Tools

Get recipes for installing development tools and libraries on various platforms for the Python library.
Authored by Sunny G. (Intel) Last updated on 05/08/2018 - 10:50
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Benefits of Intel® Optimized Caffe* in comparison with BVLC Caffe*

Overview
Authored by JON J K. (Intel) Last updated on 05/30/2018 - 07:00
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Accelerating Compute-Intensive Workloads with Intel® Advanced Vector Extensions 512 (Intel® AVX-512) Using Microsoft Visual Studio*

Some examples of how Intel® Advanced Vector Extensions 512 (Intel® AVX-512) provides performance benefits when using Microsoft Visual Studio* 2017
Authored by Junfeng D. (Intel) Last updated on 04/30/2019 - 10:10
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Virtual Vector Function Supported in Intel® C++ Compiler 17.0

Intel® C++ Compiler 17.0 starts supporting virtual vector functions.

Authored by Chen, Yuan (Intel) Last updated on 10/01/2019 - 13:05
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Improve Performance with Vectorization

This article focuses on the steps to improve software performance with vectorization. Included are examples of full applications along with some simpler cases to illustrate the steps to vectorization.
Authored by David M. Last updated on 10/15/2019 - 15:30
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Recognize and Measure Vectorization Performance

Get a background on vectorization and learn different techniques to evaluate its effectiveness.
Authored by David M. Last updated on 10/15/2019 - 15:30
Blog post

Intel® Xeon Phi™ Product Family x200 (KNL) User mode (ring 3) MONITOR and MWAIT

The Intel® Xeon Phi™ Product Family x200 series processors (formerly known as “Knights Landing”) contain a model specific feature, which allows the MONITOR and MWAIT

Authored by Cownie, James H (Intel) Last updated on 10/15/2019 - 15:50
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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 10/15/2019 - 16:40