Size Matters: Space/Time Tradeoffs to Improve GPGPU Applications Performance
SESSION: GPGPU Performance
EVENT TYPE: Paper
TIME: 2:00PM - 2:30PM
SESSION CHAIR: Gerhard Wellein
AUTHOR(S):Abdullah Gharaibeh, Matei Ripeanu
ROOM:391-392
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.
Chair/Author Details:
Gerhard Wellein (Chair) - Erlangen Regional Computing Center
Abdullah Gharaibeh - University of British Columbia
