SCHEDULE: NOV 12-18, 2011

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SSS：a MapReduce Framework based on Distributed Key-value Store

SESSION: Research Poster Reception

EVENT TYPE: ACM Student Research Competition Poster, Poster, Electronic Poster

TIME: 5:15PM - 7:00PM

SESSION CHAIR: Bernd Mohr

AUTHOR(S):Hidemoto Nakada, Hirotaka Ogawa, Tomohiro Kudoh

ROOM:WSCC North Galleria 2nd/3rd Floors

ABSTRACT:
MapReduce has been very successful in implementing large-scale data-intensive applications. Because of its simple programming model, MapReduce has also begun being utilized as a programming tool for more general distributed and parallel HPC applications. However, its applicability is often limited due to relatively inefficient runtime performance and hence insufficient support for flexible workflows. In particular, the performance problem is not negligible in iterative MapReduce applications. We implemented new MapReduce framework SSS based on distributed key-value store, that supports flexible workflows. Mappers and reducers read key-values only from its local storage enjoying high throughput and low latency. We evaluated SSS comparing with Hadoop using synthetic benchmark and real application. The result showed that SSS is significantly faster than Hadoop, especially for shuffle-intensive jobs and iterative jobs.

Chair/Author Details:

Bernd Mohr (Chair) - Juelich Supercomputing Centre

Hidemoto Nakada - National Institute of Advanced Industrial Science & Technology

Hirotaka Ogawa - National Institute of Advanced Industrial Science & Technology

Tomohiro Kudoh - National Institute of Advanced Industrial Science & Technology

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