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The VLDB Journal
Published in: The VLDB Journal 5/2017

21-08-2017 | Regular Paper

A distributed in-memory key-value store system on heterogeneous CPU–GPU cluster

Authors: Kai Zhang, Kaibo Wang, Yuan Yuan, Lei Guo, Rubao Li, Xiaodong Zhang, Bingsheng He, Jiayu Hu, Bei Hua

Published in: The VLDB Journal | Issue 5/2017

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Abstract

In-memory key-value stores play a critical role in many data-intensive applications to provide high-throughput and low latency data accesses. In-memory key-value stores have several unique properties that include (1) data-intensive operations demanding high memory bandwidth for fast data accesses, (2) high data parallelism and simple computing operations demanding many slim parallel computing units, and (3) a large working set. However, our experiments show that homogeneous multicore CPU systems are increasingly mismatched to the special properties of key-value stores because they do not provide massive data parallelism and high memory bandwidth; the powerful but the limited number of computing cores does not satisfy the demand of the unique data processing task; and the cache hierarchy may not well benefit to the large working set. In this paper, we present the design and implementation of Mega-KV, a distributed in-memory key-value store system on a heterogeneous CPU–GPU cluster. Effectively utilizing the high memory bandwidth and latency hiding capability of GPUs, Mega-KV provides fast data accesses and significantly boosts overall performance and energy efficiency over the homogeneous CPU architectures. Mega-KV shows excellent scalability and processes up to 623-million key-value operations per second on a cluster installed with eight CPUs and eight GPUs, while delivering an efficiency of up to 299-thousand operations per Watt (KOPS/W).
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Metadata
Title
A distributed in-memory key-value store system on heterogeneous CPU–GPU cluster
Authors
Kai Zhang
Kaibo Wang
Yuan Yuan
Lei Guo
Rubao Li
Xiaodong Zhang
Bingsheng He
Jiayu Hu
Bei Hua
Publication date
21-08-2017
Publisher
Springer Berlin Heidelberg
Published in
The VLDB Journal / Issue 5/2017
Print ISSN: 1066-8888
Electronic ISSN: 0949-877X
DOI
https://doi.org/10.1007/s00778-017-0479-0

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