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Solutions For Optimizing The Stream Compaction Algorithmic Function Using The Compute Unified Device Architecture

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  • Alexandru Pîrjan

    ()
    (Faculty of Computer Science for Business Management, Romanian-American University)

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    Abstract

    In this paper, I have researched and developed solutions for optimizing the stream compaction algorithmic function using the Compute Unified Device Architecture (CUDA). The stream compaction is a common parallel primitive, an essential building block for many data processing algorithms, whose optimization improves the performance of a wide class of parallel algorithms useful in data processing. A particular interest in this research was to develop solutions for optimizing the stream compaction algorithmic function that offers optimal solutions over an entire range of CUDA enabled GPUs: Tesla GT200, Fermi GF100 and the latest Kepler GK104 architecture, released on 22 March 2012. In order to confirm the utility of the developed optimization solutions, I have extensively benchmarked and evaluated the performance of the stream compaction algorithmic function in CUDA.

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    File URL: http://www.rebe.rau.ro/RePEc/rau/jisomg/SP12/JISOM-SP12-A20.pdf
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    Bibliographic Info

    Article provided by Romanian-American University in its journal Journal of Information Systems and Operations Management.

    Volume (Year): 6 (2012)
    Issue (Month): 1 (May)
    Pages: 216-231

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    Handle: RePEc:rau:journl:v:6:y:2012:i:1:p:216-231

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    Related research

    Keywords: parallel processing; CUDA; Kepler; threads; stream compaction;

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    1. Alexandru PIRJAN, 2010. "Improving Software Performance in the Compute Unified Device Architecture," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 14(4), pages 30-47.
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