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

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

作者机构:Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA 

出 版 物:《IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS》 (IEEE Trans Parallel Distrib Syst)

年 卷 期:2010年第21卷第1期

页      面:33-46页

核心收录:

学科分类:0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术(可授工学、理学学位)] 

基  金:US National Science Foundation [CCF-06-21434  CNS-06-43229  CNS-01-30839] 

主  题:All-pairs biometrics cloud computing data intensive computing grid computing 

摘      要: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.

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