title: An Integrated Framework for Parameter-based Optimization of 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 multi-dimensional parameter space. While some performance parameters such as grouping of workflow components and their mapping to machines do not a ect the accuracy of the output, 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 dimensions of the parameter space. Using two real-world applications in the spatial data analysis domain, we present an experimental evaluation of the proposed framework.
authors: Vijay S
authors: VS
authors: Kumar
authors: P
authors: P
authors: Sadayappan
authors: Gaurang
authors: G
authors: Mehta
authors: Karan
authors: K
authors: Vahi
authors: Ewa
authors: E
authors: Deelman
authors: Varun
authors: V
authors: Ratnakar
authors: Jihie
authors: J
authors: Kim
authors: Yolanda
authors: Y
authors: Gil
authors: Mary
authors: M
authors: Hall
authors: Tahsin
authors: T
authors: Kurc
authors: Joel
authors: J
authors: Saltz