SCHEDULE: NOV 16-22, 2013
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Effective Sampling-Driven Performance Tools for GPU-Accelerated Supercomputers
SESSION: Tools for Scalable Analysis
EVENT TYPE: Papers
TIME: 3:30PM - 4:00PM
SESSION CHAIR: Dorian C. Arnold
AUTHOR(S):Milind Chabbi, Karthik Murthy, Michael Fagan, John Mellor-Crummey
Performance analysis of GPU-accelerated systems requires a system-wide view that considers both CPU and GPU components. In this paper, we describe how to extend system-wide, sampling-based performance analysis methods to GPU-accelerated systems. Since current GPUs do not support sampling, our implementation required careful coordination of instrumentation-based performance data collection on GPUs with sampling-based methods employed on CPUs. In addition, we also introduce a novel technique for analyzing systemic idleness in CPU/GPU systems. We demonstrate the effectiveness of our techniques with application case studies on Titan and Keeneland. Some of the highlights of our case studies are: 1) we improved performance for LULESH 1.0 by 30%, 2) we identified a hardware performance problem on Keeneland, 3) we identified a scaling problem in LAMMPS derived from CUDA initialization, and 4) we identified a performance problem that is caused by GPU synchronization operations that suffer delays due to blocking system calls.
Dorian C. Arnold (Chair) - University of New Mexico
Milind Chabbi - Rice University
Karthik Murthy - Rice University
Michael Fagan - Rice University
John Mellor-Crummey - Rice University
The full paper can be found in the ACM Digital Library