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All-pairs: An abstraction for data-intensive cloud computing

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4 Author(s)
Moretti, C. ; Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, Notre Dame, IN ; Bulosan, J. ; Thain, D. ; Flynn, P.J.

Although modern parallel and distributed computing systems provide easy access to large amounts of computing power, it is not always easy for non-expert users to harness these large systems effectively. A large workload composed in what seems to be the obvious way by a naive user may accidentally abuse shared resources and achieve very poor performance. To address this problem, we propose that production systems should provide end users with high-level abstractions that allow for the easy expression and efficient execution of data intensive workloads. We present one example of an abstraction - all-pairs - that fits the needs of several data-intensive scientific applications. We demonstrate that an optimized all-pairs abstraction is both easier to use than the underlying system, and achieves performance orders of magnitude better than the obvious but naive approach, and twice as fast as a hand-optimized conventional approach.

Published in:

Parallel and Distributed Processing, 2008. IPDPS 2008. IEEE International Symposium on

Date of Conference:

14-18 April 2008