Parameterized specification, configuration and execution of data-intensive scientific workflows

  • Authors:

  • Vijay S. Kumar;Tahsin Kurc;Varun Ratnakar;Jihie Kim;Gaurang Mehta;Karan Vahi;Yoonju Lee Nelson;P. Sadayappan;Ewa Deelman;Yolanda Gil;Mary Hall;Joel Saltz

  • Affiliations:

  • Dept. of Computer Science and Engineering, Ohio State University, Columbus, USA 43210;Center for Comprehensive Informatics, Emory University, Atlanta, USA 30322;Information Sciences Institute, University of Southern California, Marina del Rey, USA 90292;Information Sciences Institute, University of Southern California, Marina del Rey, USA 90292;Information Sciences Institute, University of Southern California, Marina del Rey, USA 90292;Information Sciences Institute, University of Southern California, Marina del Rey, USA 90292;Information Sciences Institute, University of Southern California, Marina del Rey, USA 90292;Dept. of Computer Science and Engineering, Ohio State University, Columbus, USA 43210;Information Sciences Institute, University of Southern California, Marina del Rey, USA 90292;Information Sciences Institute, University of Southern California, Marina del Rey, USA 90292;School of Computing, University of Utah, Salt Lake City, USA 84112;Center for Comprehensive Informatics, Emory University, Atlanta, USA 30322

  • Venue:
  • Cluster Computing
  • Year:
  • 2010

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Abstract

Data analysis processes in scientific applications can be expressed as coarse-grain workflows of complex data processing operations with data flow dependencies between them. Performance optimization of these workflows can be viewed as a search for a set of optimal values in a multidimensional parameter space consisting of input performance parameters to the applications that are known to affect their execution times. While some performance parameters such as grouping of workflow components and their mapping to machines do not affect the accuracy of the analysis, others may dictate trading the output quality of individual components (and of the whole workflow) for performance. This paper describes an integrated framework which is capable of supporting performance optimizations along multiple such parameters. Using two real-world applications in the spatial, multidimensional data analysis domain, we present an experimental evaluation of the proposed framework.