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Document
Invited Talk
Forward Progress on GPU Concurrency (Invited Talk)

Authors: Alastair F. Donaldson, Jeroen Ketema, Tyler Sorensen, and John Wickerson

Published in: LIPIcs, Volume 85, 28th International Conference on Concurrency Theory (CONCUR 2017)


Abstract
The tutorial at CONCUR will provide a practical overview of work undertaken over the last six years in the Multicore Programming Group at Imperial College London, and with collaborators internationally, related to understanding and reasoning about concurrency in software designed for acceleration on GPUs. In this article we provide an overview of this work, which includes contributions to data race analysis, compiler testing, memory model understanding and formalisation, and most recently efforts to enable portable GPU implementations of algorithms that require forward progress guarantees.

Cite as

Alastair F. Donaldson, Jeroen Ketema, Tyler Sorensen, and John Wickerson. Forward Progress on GPU Concurrency (Invited Talk). In 28th International Conference on Concurrency Theory (CONCUR 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 85, pp. 1:1-1:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)


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@InProceedings{donaldson_et_al:LIPIcs.CONCUR.2017.1,
  author =	{Donaldson, Alastair F. and Ketema, Jeroen and Sorensen, Tyler and Wickerson, John},
  title =	{{Forward Progress on GPU Concurrency}},
  booktitle =	{28th International Conference on Concurrency Theory (CONCUR 2017)},
  pages =	{1:1--1:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-048-4},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{85},
  editor =	{Meyer, Roland and Nestmann, Uwe},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CONCUR.2017.1},
  URN =		{urn:nbn:de:0030-drops-78055},
  doi =		{10.4230/LIPIcs.CONCUR.2017.1},
  annote =	{Keywords: GPUs, concurrency, formal verification, memory models, data races}
}
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