Characterizing and evaluating a key-value store application on heterogeneous CPU-GPU systems

• Published in: 2012 IEEE International Symposium on Performance Analysis of Systems & Software pp. 88 - 98
• Main Authors: Hetherington, T. H., Rogers, T. G., Hsu, L., O'Connor, M., Aamodt, T. M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-04-2012
• Subjects: GPGPU; Graphics processing unit; Hardware; Kernel; key-value store; Memory management; OpenCL; Payloads; server; Servers; SIMD
• ISBN: 146731143X; 9781467311434
• DOI: 10.1109/ISPASS.2012.6189209

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.