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.
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Przymus, P., Kaczmarski, K. (2012). Improving Efficiency of Data Intensive Applications on GPU Using Lightweight Compression. In: Herrero, P., Panetto, H., Meersman, R., Dillon, T. (eds) On the Move to Meaningful Internet Systems: OTM 2012 Workshops. OTM 2012. Lecture Notes in Computer Science, vol 7567. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33618-8_2
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DOI: https://doi.org/10.1007/978-3-642-33618-8_2
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