Combining Linux* Message Passing and Threading in High Performance Computing

An article addressing thread and task parallelism. This article can be used to optimize framework methodology. Written by Andrew Binstock--Principal Analyst at Pacific Data Works LLC and lead author of "Practical Algorithms for Programmers."
Criado por Última atualização em 06/07/2019 - 16:22

Threading Models for High-Performance Computing: Pthreads or OpenMP?

In recent years, Linux* has bolster its presence on the server, due to improved kernel support for threads. Along the way, Linux abandoned its original threading API (called Linux threads) and adopted Pthreads as its native threading interface, joining most of the UNIX variants available today. Linux developers-just like programmers working on UNIX and Windows*-can avail themselves of a second...
Criado por Última atualização em 06/07/2019 - 16:40

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.
Criado por Andrey Karpov (Blackbelt) Última atualização em 20/06/2019 - 22:51

Classical Molecular Dynamics Simulations with LAMMPS Optimized for Knights Landing

LAMMPS is an open-source software package that simulates classical molecular dynamics. As it supports many energy models and simulation options, its versatility has made it a popular choice. It was first developed at Sandia National Laboratories to use large-scale parallel computation.
Criado por WILLIAM B. (Intel) Última atualização em 21/03/2019 - 12:00

Accelerating Deep Learning Based Large-Scale Inverse Kinematics with Intel® Distribution of OpenVINO™ Toolkit

Use Deep Learning Deployment Toolkit (DLDT) to deploy deep-learning algorithms to solve character Inverse Kinematics (IK) problems.
Criado por Tai Ha (Intel) Última atualização em 19/09/2019 - 10:35

英特尔® OpenVINO™ 工具套件分发版助力加速基于深度学习的大规模反向运动学

使用深度学习部署工具套件 (DLDT) 部署深度学习算法,以解决角色的反向运动学 (IK) 问题。
Criado por Tai Ha (Intel) Última atualização em 19/09/2019 - 10:37