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PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:2.0
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DTSTART:20151118T220000Z
DTEND:20151118T223000Z
LOCATION:18CD
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: In-situ analytics has lately been shown to be an effective approach to reduce both I/O and storage costs. Developing an efficient in-situ implementation involves many challenges, including parallelization, data movement or sharing, and resource allocation. Although MapReduce has been widely adopted for parallelizing data analytics, there are several obstacles to applying it to in-situ scientific analytics.=0A=0AIn this paper, we present a novel MapReduce-like framework which supports efficient in-situ scientific analytics. It can load simulated data directly from distributed memory. It leverages a MapReduce-like API for parallelization, while meeting the strict memory constraints of in-situ analytics. It can be launched in the parallel code region of simulation program. We have developed both space sharing and time sharing modes for maximizing the performance in different scenarios. We demonstrate both high efficiency and scalability of our system, by using different simulation and analytics programs on both multi-core and many-core clusters.
SUMMARY:Smart: A MapReduce-Like Framework for In-Situ Scientific Analytics
PRIORITY:3
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