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Size Matters: Space/Time Tradeoffs to Improve GPGPU Applications Performance

Abdullah Gharaibeh, Matei Ripeanu
Center for Bioinformatics and Computational Biology, University of Maryland, MD 20740, United States
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, SC ’10, November 2010, New Orleans, Louisiana, USA

@article{gharaibeh2010size,

   title={Size Matters: Space/Time Tradeoffs to Improve GPGPU Applications Performance},

   author={Gharaibeh, A. and Ripeanu, M.},

   journal={sc},

   pages={1–12},

   year={2010},

   publisher={IEEE Computer Society}

}

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GPUs offer drastically different performance characteristics compared to traditional multicore architectures. To explore the tradeoffs exposed by this difference, we refactor MUMmer, a widely-used, highly-engineered bioinformatics application which has both CPU- and GPU-based implementations. We synthesize our experience as three high-level guidelines to design efficient GPU-based applications. First, minimizing the communication overheads is as important as optimizing the computation. Second, trading-off higher computational complexity for a more compact in-memory representation is a valuable technique to increase overall performance (by enabling higher parallelism levels and reducing transfer overheads). Finally, ensuring that the chosen solution entails low pre- and post-processing overheads is essential to maximize the overall performance gains. Based on these insights, MUMmerGPU++, our GPU-based design of the MUMmer sequence alignment tool, achieves, on realistic workloads, up to 4x speedup compared to a previous, highly optimized GPU port.
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