All-Pairs: An Abstraction for Data Intensive Computing on Campus Grids

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Executive Summary

Today, campus grids provide users with easy access to thousands of CPUs. However, it is not always easy for non-expert users to harness these 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, the authors argue that campus grids should provide end users with high-level abstractions that allow for the easy expression and efficient execution of data intensive workloads. They present one example of an abstraction - All-Pairs - that fits the needs of several applications in biometrics, bioinformatics, and data mining.

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