Issue No. 01 - January (2010 vol. 21)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2009.49
Christopher Moretti , University of Notre Dame, Notre Dame
Hoang Bui , University of Notre Dame, Notre Dame
Karen Hollingsworth , University of Notre Dame, Notre Dame
Brandon Rich , University of Notre Dame, Notre Dame
Patrick Flynn , University of Notre Dame, Notre Dame
Douglas Thain , University of Notre Dame, Notre Dame
Today, campus grids provide users with easy access to thousands of CPUs. However, it is not always easy for nonexpert 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, we 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. We present one example of an abstraction—All-Pairs—that fits the needs of several applications in biometrics, bioinformatics, and data mining. We demonstrate that an optimized All-Pairs abstraction is both easier to use than the underlying system, achieve performance orders of magnitude better than the obvious but naive approach, and is both faster and more efficient than a tuned conventional approach. This abstraction has been in production use for one year on a 500 CPU campus grid at the University of Notre Dame and has been used to carry out a groundbreaking analysis of biometric data.
All-pairs, biometrics, cloud computing, data intensive computing, grid computing.
H. Bui, P. Flynn, D. Thain, C. Moretti, B. Rich and K. Hollingsworth, "All-Pairs: An Abstraction for Data-Intensive Computing on Campus Grids," in IEEE Transactions on Parallel & Distributed Systems, vol. 21, no. , pp. 33-46, 2009.