Characterizing and Evaluating a Key-value Store Application on Heterogeneous CPU-GPU Systems
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), 2012
@article{hetherington2012characterizing,
title={Characterizing and Evaluating a Key-value Store Application on Heterogeneous CPU-GPU Systems},
author={Hetherington, Tayler H. and Rogers, Timothy G. and Hsu, Lisa and O’Connor, Mike and Aamodt, Tor M.},
year={2012}
}
The recent use of graphics processing units (GPUs) in several top supercomputers demonstrate their ability to consistently deliver positive results in high-performance computing (HPC). GPU support for significant amounts of parallelism would seem to make them strong candidates for non-HPC applications as well. Server workloads are inherently parallel; however, at first glance they may not seem suitable to run on GPUs due to their irregular control flow and memory access patterns. In this work, we evaluate the performance of a widely used key-value store middleware application, Memcached, on recent integrated and discrete CPU+GPU heterogeneous hardware and characterize the resulting performance. To gain greater insight, we also evaluate Memcached’s performance on a GPU simulator. This work explores the challenges in porting Memcached to OpenCL and provides a detailed analysis into Memcached’s behavior on a GPU to better explain the performance results observed on physical hardware. On the integrated CPU+GPU systems, we observe up to 7.5X performance increase compared to the CPU when executing the key-value look-up handler on the GPU.
February 11, 2012 by hgpu
