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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 05/30/2018 - 07:00
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Explicit Vector Programming in Fortran

No longer does Moore’s Law result in higher frequencies and improved scalar application performance; instead, higher transistor counts lead to increased parallelism, both through more cores and thr

Authored by Martyn Corden (Intel) Last updated on 03/27/2019 - 15:50
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Peel the Onion (Optimization Techniques)

This paper is a more formal response to an Intel® Developer Zone forum posting. See: (https://software.intel.com/en-us/forums/intel-moderncode-for-parallel-architectures/topic/590710).
Authored by jimdempseyatthecove (Blackbelt) Last updated on 12/12/2018 - 18:00
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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 06/01/2017 - 11:32
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Чистим лук (но не плачем): методики оптимизации

Эта статья представляет собой формализованный ответ на публикацию на форуме Intel® Developer Zone. См.: (https://software.intel.com/en-us/forums/intel-moderncode-for-parallel-architectures/topic/590710).
Authored by Last updated on 12/12/2018 - 18:00
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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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OpenMP* SIMD for Inclusive/Exclusive Scans

The Intel® Compiler 19.0 supports the OpenMP* SIMD SCAN feature for inclusive and exclusive scans.
Authored by Varsha M. (Intel) Last updated on 02/08/2019 - 08:58
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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
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90 errors in open-source projects

There are actually 91 errors described in the article, but number 90 looks nicer in the title. The article is intended for C/C++ programmers, but developers working with other languages may also find it interesting.
Authored by Andrey Karpov (Blackbelt) Last updated on 06/20/2019 - 22:51
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Using Tasks Instead of Threads

Tasks are a lightweight alternative to threads that provide faster startup and shutdown times, better load balancing, an efficient use of available resources, and a higher level of abstraction.
Authored by admin Last updated on 07/05/2019 - 09:41