SCHEDULE: NOV 15-20, 2015

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Smart: A MapReduce-Like Framework for In-Situ Scientific Analytics

SESSION: In-Situ (Simulation Time) Analysis

EVENT TYPE: Papers

EVENT TAG(S): Applications, Analytics, Scientific Computing, Simulation

TIME: 4:00PM - 4:30PM

SESSION CHAIR(S): Suren Byna

AUTHOR(S):Yi Wang, Gagan Agrawal, Tekin Bicer, Wei Jiang

ROOM:18CD

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.

In 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.

Chair/Author Details:

Suren Byna (Chair) - Lawrence Berkeley National Laboratory|

Yi Wang - Ohio State University

Gagan Agrawal - Ohio State University

Tekin Bicer - Argonne National Laboratory

Wei Jiang - Quantcast

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society