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Introduction to OpenMP* on YouTube*

Tim Mattson (Intel) has authored an extensive series of excellent videos as in introduction to OpenMP*.

Автор: Mike P. (Intel) Последнее обновление: 04.07.2019 - 19:51
Article

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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Big Datasets from Small Experiments

Автор: Andrey Vladimirov Последнее обновление: 04.07.2019 - 18:46
Блоги

Brain Development Simulation, 300x Faster

Автор: Andrey Vladimirov Последнее обновление: 04.07.2019 - 17:45
Блоги

Track Reconstruction with Deep Learning at the CERN CMS Experiment

Connecting the Dots
Автор: Последнее обновление: 12.12.2018 - 18:00
Article

Free access to Intel® Compilers, Performance libraries, Analysis tools and more...

Intel® Parallel Studio XE is a very popular product from Intel that includes the Intel® Compilers, Intel® Performance Libraries, tools for analysis, debugging and tuning, tools for MPI and the Intel® MPI Library. Did you know that some of these are available for free? Here is a guide to “what is available free” from the Intel Parallel Studio XE suites.
Автор: админ Последнее обновление: 30.09.2019 - 17:28
Article

Case Study: Optimized Code for Neural Cell Simulations

One of the Intel® Modern Code Developer Challenge winners, Daniel Falguera, describes many of the optimizations he implemented and why some didn't work.
Автор: Последнее обновление: 03.10.2019 - 07:55
Article

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
Article

Caffe* Optimized for Intel® Architecture: Applying Modern Code Techniques

This paper demonstrates a special version of Caffe* — a deep learning framework originally developed by the Berkeley Vision and Learning Center (BVLC) — that is optimized for Intel® architecture.
Автор: Последнее обновление: 15.10.2019 - 15:30
Article

GROMACS Recipe for Symmetric Intel® MPI Using PME Workloads

Objectives
Автор: Heinrich Bockhorst (Intel) Последнее обновление: 15.10.2019 - 16:40