Job Parallelism using Graphical Processing Unit Individual Multi-Processors and Localised Memory

D.P. Playne, K.A. Hawick
Computer Science, Massey University, North Shore 102-904, Auckland, New Zealand
The 2013 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA’13), 2013

   title={Job Parallelism using Graphical Processing Unit Individual Multi-Processors and Localised Memory},

   author={Playne, DP and Hawick, KA},



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Graphical Processing Units(GPUs) are usually programmed to provide data-parallel acceleration to a host processor. Modern GPUs typically have an internal multi-processor (MP) structure that can be exploited in an unusual way to offer semi-independent task parallelism providing the MPs can operate within their own localised memory and apply data-parallelism to their own problem subset. We describe a combined simulation and statistical analysis application using component labelling and benchmark it on a range of modern GPU and CPU devices with various numbers of cores. As well as demonstrating a high degree of job parallelism and throughput we find a typical GPU MP outperforms a conventional CPU core.
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