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X-WR-CALNAME:Size Matters: Space/Time Tradeoffs to Improve GPGPU Applications Performance
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TZID:America/Chicago
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DTSTART:20070311T020000
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SEQUENCE:2
DTSTART;TZID=America/Chicago:20101118T140000
DESCRIPTION:ABSTRACT: 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.
UID:pap221@sc10.supercomputing.org
SUMMARY:Size Matters: Space/Time Tradeoffs to Improve GPGPU Applications Performance
DTEND;TZID=America/Chicago:20101118T143000
LOCATION:391-392
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