Dynamic Kernel/Device Mapping Strategies for GPU-assisted HPC Systems
School of Electric and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332
16th Workshop on Job Scheduling Strategies for Parallel Processing, held in conjunction with the 26th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2012), 2012
@article{wu2012dynamic,
title={Dynamic Kernel/Device Mapping Strategies for GPU-assisted HPC Systems},
author={Wu, J. and Shi, W. and Hong, B.},
year={2012}
}
With their high computation throughput and outstanding performance-per-watt figures, the graphics processing units (GPU) are becoming increasingly important for high-performance computing (HPC) systems. Existing GPU execution environment restricts the GPU usage to local host node. This is suitable for standalone computer nodes, but becomes inefficient for HPC systems that consist of a large number of GPU-assisted nodes. In this paper, a novel framework is proposed to support dynamic GPU kernel/device mapping strategies for HPC systems. Adaptive mapping policies are designed to mitigate the impact of network transfer overhead. The performance of the framework is studied through extensive simulations. The results show that compared with existing local-only static mapping method, the proposed framework is capable of improving the system-wide GPU utilization rate and computation throughput, especially when the concurrent workloads exhibit different GPU usage intensities.
May 2, 2012 by hgpu
