<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fábio Soldado</style></author><author><style face="normal" font="default" size="100%">Fernando Alexandre</style></author><author><style face="normal" font="default" size="100%">Paulino, Hervé</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards the Transparent Execution of Compound OpenCL Computations in Multi-CPU/Multi-GPU Environments</style></title><secondary-title><style face="normal" font="default" size="100%">Euro-Par 2014 International Workshops, Revised Selected Papers, Part I</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">25-29 August</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://citi.di.fct.unl.pt/publication/inproceeding.php?id=909</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Porto, Portugal</style></pub-location><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Currentcomputationalsystemsareheterogeneousbynature, featuring a combination of CPUs and GPUs. As the latter are becoming an established platform for high-performance computing, the focus is shifting towards the seamless programming of the heterogeneous systems as a whole. The distinct nature of the architectural and execution models in place raise several challenges, as the best hardware configuration is behavior and data-set dependent. In this paper, we focus the execution of compound computations in multi-CPU/multi-GPU environments, in the scope of Marrow algorithmic skeleton framework, the only, to the best of our knowledge, to support skeleton nesting in GPU computing. We address how these computations may be efficiently scheduled onto the target hardware, and how the system may adapt itself to changes in the CPU’s load and in the input data-set.&lt;/p&gt;
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