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Characterizing and Evaluating a Key-value Store Application on Heterogeneous CPU-GPU Systems

Tayler H. Hetherington, Timothy G. Rogers, Lisa Hsu, Mike O’Connor, Tor M. Aamodt
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}

}

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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.
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