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Graphics processing unit (GPU) programming strategies and trends in GPU computing

Andre R. Brodtkorb, Trond R. Hagen, Martin L. Saetra
SINTEF, Dept. Appl. Math., P.O. Box 124, Blindern, NO-0314 Oslo, Norway
Journal of Parallel and Distributed Computing, Volume 73, Issue 1, Pages 4-13, 2013
@article{Brodtkorb20134,

   title={"Graphicsprocessingunit(GPU)programmingstrategiesandtrendsin{GPU}computing"},

   journal={"JournalofParallelandDistributedComputing"},

   volume={"73"},

   number={"1"},

   pages={"4-13"},

   year={"2013"},

   issn={"0743-7315"},

   doi={"10.1016/j.jpdc.2012.04.003"},

   url={"http://www.sciencedirect.com/science/article/pii/S0743731512000998"},

   author={"AndreR.BrodtkorbandTrondR.HagenandMartinL.Saetra"},

   keywords={"Futuretrends"}

}

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Over the last decade, there has been a growing interest in the use of graphics processing units (GPUs) for non-graphics applications. From early academic proof-of-concept papers around the year 2000, the use of GPUs has now matured to a point where there are countless industrial applications. Together with the expanding use of GPUs, we have also seen a tremendous development in the programming languages and tools, and getting started programming GPUs has never been easier. However, whilst getting started with GPU programming can be simple, being able to fully utilize GPU hardware is an art that can take months or years to master. The aim of this article is to simplify this process, by giving an overview of current GPU programming strategies, profile-driven development, and an outlook to future trends.
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