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SCHEDULE: NOV 12-18, 2011

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I/O Streaming Evaluation of Batch Queries for Data-Intensive Computational Turbulence

SESSION: Querying Large Scale Data

EVENT TYPE: Paper

TIME: 3:30PM - 4:00PM

AUTHOR(S):Kalin Kanov, Eric Perlman, Randal Burns, Yanif Ahmad, Alexander Szalay

ROOM:TCC 305

ABSTRACT:
We describe a method for evaluating computational turbulence queries, including Lagrange Polynomial interpolation, based on partial sums that allows the underlying data to be accessed in any order and in parts. We exploit these properties to stream data from disk in a single pass and concurrently evaluate batch queries. The combination of sequential I/O and data sharing improves performance by an order of magnitude when compared with direct evaluation of each query. The technique also supports distributed evaluation of queries in a database cluster, assembling the partial sums from each node at the query mediator. Interpolation is fundamental to computational turbulence, over 95% of queries use these routines, and the partial sums method allows the JHU Turbulence Database Cluster to realize scale and throughput for our scientists' data-intensive workloads.

Chair/Author Details:

Kalin Kanov - Johns Hopkins University

Eric Perlman - Johns Hopkins University

Randal Burns - Johns Hopkins University

Yanif Ahmad - Johns Hopkins University

Alexander Szalay - Johns Hopkins University

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The full paper can be found in the ACM Digital Library

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