Size Matters: Space/Time Tradeoffs to Improve GPGPU Applications Performance
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
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.
February 27, 2011 by hgpu
