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Using Intel® MKL and Intel® TBB in the same application

Intel MKL 11.3 has introduced Intel TBB support.

Автор: Gennady F. (Blackbelt) Последнее обновление: 01.08.2019 - 09:22
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Improve Vectorization Performance with Intel® AVX-512

See how the new Intel® Advanced Vector Extensions 512CD and the Intel AVX512F subsets (available in the Intel® Xeon Phi processor and in future Intel Xeon processors) lets the compiler automatically generate vector code with no changes to the code.
Автор: Alberto V. (Intel) Последнее обновление: 08.07.2019 - 19:26
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Machine Learning and Knowledge Reasoning Probing with Intel® Architecture

This article presents a performance test of Intel® tuning platform composed by Intel® processor, Intel® C++ Compiler XE and Intel® Math Kernel Library (Intel® MKL) applied to usual AI problems, with two existing approaches of artificial intelligence probed.
Автор: Flavio Luis de Mello Последнее обновление: 24.01.2018 - 15:35
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Enabling Intel® MKL in PETSc applications

 
Автор: Gennady F. (Blackbelt) Последнее обновление: 24.05.2018 - 15:48
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Unpack result of Intel® MKL FFT to align with Matlab

Introduction Overview
Автор: Jing X. (Intel) Последнее обновление: 28.05.2019 - 00:55
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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.
Автор: Последнее обновление: 15.10.2019 - 15:30