Improving Efficiency of Data Intensive Applications on GPU Using Lightweight Compression

  • Piotr Przymus
  • Krzysztof Kaczmarski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7567)

Abstract

In many scientific and industrial applications GPGPU (General-Purpose Computing on Graphics Processing Units) programming reported excellent speed-up when compared to traditional CPU (central processing unit) based libraries. However, for data intensive applications this benefit may be much smaller or may completely disappear due to time consuming memory transfers. Up to now, gain from processing on the GPU was noticeable only for problems where data transfer could be compensated by calculations, which usually mean large data sets and complex computations. This paper evaluates a new method of data decompression directly in GPU shared memory which minimizes data transfers on the path from disk, through main memory, global GPU device memory, to GPU processor. The method is successfully applied to pattern matching problems. Results of experiments show considerable speed improvement for large and small data volumes which is a significant step forward in GPGPU computing.

Keywords

lightweight compression data-intensive computations GPU CUDA 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Piotr Przymus
    • 1
  • Krzysztof Kaczmarski
    • 2
  1. 1.Nicolaus Copernicus UniversityToruńPoland
  2. 2.Warsaw University of TechnologyWarszawaPoland

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