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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)

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.

Suggested Citation

  • Alexandru Pîrjan, 2012. "Solutions For Optimizing The Stream Compaction Algorithmic Function Using The Compute Unified Device Architecture," Romanian Economic Business Review, Romanian-American University, vol. 6(1), pages 216-231, May.
  • Handle: RePEc:rau:journl:v:6:y:2012:i:1:p:216-231
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