Digital Sherpa: a set of high level tools to manage scientific applications in a computational grid

Authors:
Ronald C. Price;Wayne B. Bradford;Victor E. Bazterra;Julio C. Facelli
Affiliations:
University of Utah, Salt Lake City, Utah;University of Utah, Salt Lake City, Utah;University of Utah, Salt Lake City, Utah;University of Utah, Salt Lake City, Utah
Venue:
Proceedings of the 15th ACM Mardi Gras conference: From lightweight mash-ups to lambda grids: Understanding the spectrum of distributed computing requirements, applications, tools, infrastructures, interoperability, and the incremental adoption of key capabilities
Year:
2008

Citing 3
Cited 0

Grid Computing: Making the Global Infrastructure a Reality

Grid Computing: Making the Global Infrastructure a Reality
A general framework to understand parallel performance in heterogeneous clusters: analysis of a new adaptive parallel genetic algorithm

Journal of Parallel and Distributed Computing
Scaling System-Level Science: Scientific Exploration and IT Implications

Computer

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Abstract

Currently users of high performance computers are overwhelmed with non scalable tasks such as job submission and monitoring, a problem that gets compounded when trying to run complex scientific applications requiring the coordination of several interrelated programs. Digital Sherpa (DS) is a grid tool set for coordinating the execution of multiple jobs on separate HPC resources; DS automates non-scalable tasks such as job submission and monitoring, and includes recovery features such as resubmission of failed jobs and program restarting. DS has been used to develop a Grid enabled version, MGAC-CGA, of the Modified Genetic Algorithms for Crystals and Clusters (MGAC), a parallel distributed application for the prediction of the structures of atomic clusters and organic crystals using Genetic Algorithms (GA). MGAC-CGA has been successfully tested on the NSF TeraGrid and on several clusters at the University of Utah.