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Rob Farber takes you on a tour of the paths to massively parallel x86, MultiGPU, and CPU+GPU applications. - Parallel
Using OpenACC to write your first hybrid application - Parallel
Both CUDA and Phi coprocessors provide high degrees of parallelism that can deliver excellent application performance. For the most part, CUDA programmers with existing application code have already written their software so it can run well on Phi coprocessors. However, additional work may be required to achieve the highest possible performance. - Parallel
The advent of Intel's massively parallel coprocessor will make every server a supercomputer. - Parallel
How to write reusable methods (libraries, subroutines, and functions) that can transparently call optimized CPU and GPU libraries using OpenACC pragmas. - Parallel
In this second part of the introduction to OpenACC the OpenMP-style library for GPU programming the execution model is explained and samples are benchmarked against straight, OpenMP parallelism. - Parallel
The advent of the many-core era is not going to push developers to write more parallel code. That hasn't happened as we've gone from 1- to 2- to 4- to 8-core processors, has it? Writing parallel code is not the problem. So, let's stop trying to fix it. - Parallel
Company adds support for AMD's heterogeneous compute platforms - Parallel
An emerging standard uses pragmas to move parallel computations in C/C++ and Fortran to the GPU - Parallel
Cross-platform API and open parallel programming standard for programmers - Open Source
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