All-pairs: An abstraction for data-intensive computing on campus grids

Christopher Moretti, Hoang Bui, Karen Hollingsworth, Brandon Rich, Patrick Flynn, Douglas Thain

Research output: Contribution to journalArticlepeer-review

51 Scopus citations


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 abstractionAll-Pairsthat 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.

Original languageEnglish (US)
Article number4803834
Pages (from-to)33-46
Number of pages14
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number1
StatePublished - Jan 2010
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics


  • All-pairs
  • Biometrics
  • Cloud computing
  • Data intensive computing
  • Grid computing


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