title: Parameterized Specification, Configuration and Execution of Data-Intensive Scientific Workflows.
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.
authors.affiliation: Dept. of Computer Science and Engineering, Ohio State University, Columbus, OH 43210.
authors.forename: Vijay S
authors.initials: VS
authors.lastname: Kumar
authors.forename: Tahsin
authors.initials: T
authors.lastname: Kurc
authors.forename: Varun
authors.initials: V
authors.lastname: Ratnakar
authors.forename: Jihie
authors.initials: J
authors.lastname: Kim
authors.forename: Gaurang
authors.initials: G
authors.lastname: Mehta
authors.forename: Karan
authors.initials: K
authors.lastname: Vahi
authors.forename: Yoonju Lee
authors.initials: YL
authors.lastname: Nelson
authors.forename: P
authors.initials: P
authors.lastname: Sadayappan
authors.forename: Ewa
authors.initials: E
authors.lastname: Deelman
authors.forename: Yolanda
authors.initials: Y
authors.lastname: Gil
authors.forename: Mary
authors.initials: M
authors.lastname: Hall
authors.forename: Joel
authors.initials: J
authors.lastname: Saltz