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MemcachedGPU: Scaling-up Scale-out Key-value Stores

Tayler H. Hetherington, Mike O’Connor, Tor M. Aamodt
The University of British Columbia
ACM Symposium on Cloud Computing (SoCC 2015), 2015

@article{hetherington2015memcachedgpu,

   title={MemcachedGPU: Scaling-up Scale-out Key-value Stores},

   author={Hetherington, Tayler H and O’Connor, Mike and Aamodt, Tor M},

   year={2015}

}

This paper tackles the challenges of obtaining more efficient data center computing while maintaining low latency, low cost, programmability, and the potential for workload consolidation. We introduce GNoM, a software framework enabling energy-efficient, latency bandwidth optimized UDP network and application processing on GPUs. GNoM handles the data movement and task management to facilitate the development of high-throughput UDP network services on GPUs. We use GNoM to develop MemcachedGPU, an accelerated key-value store, and evaluate the full system on contemporary hardware. MemcachedGPU achieves ~10 GbE line-rate processing of ~13 million requests per second (MRPS) while delivering an efficiency of 62 thousand RPS per Watt (KRPS/W) on a high-performance GPU and 84.8 KRPS/W on a lowpower GPU. This closely matches the throughput of an optimized FPGA implementation while providing up to 79% of the energy-efficiency on the low-power GPU. Additionally, the low-power GPU can potentially improve cost-efficiency (KRPS/$) up to 17% over a state-of-the-art CPU implementation. At 8 MRPS, MemcachedGPU achieves a 95-percentile RTT latency under 300µs on both GPUs. An offline limit study on the low-power GPU suggests that MemcachedGPU may continue scaling throughput and energyefficiency up to 28.5 MRPS and 127 KRPS/W respectively.
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