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Hardware acceleration of Hadoop MapReduce

Authors: Toshimori Honjo; Kazuki Oikawa;

Hardware acceleration of Hadoop MapReduce

Abstract

MapReduce is widely used for BigData processing. It was originally designed to overcome the I/O bottleneck of commodity servers. However, several high speed storage and network devices have recently emerged, and speeds continue to increase. Employing such brand new devices will solve the I/O bottleneck, making the CPU the next serious bottleneck in the MapReduce framework. In this paper, we conduct a performance study of Hadoop MapReduce by using a server cluster built on state-of-the-art devices. We show the CPU is the bottleneck in such an environment. To overcome the CPU bottleneck, we propose hardware acceleration for MapReduce. We implement a prototype using a many core processor board developed by Tilera and show the feasibility of our proposal.

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
20
Average
Top 10%
Top 10%
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