Grid-Based Problem Solving Environments: IFIP TC2/ WG 2.5 by Dennis Gannon, Marcus Christie, Suresh Marru, Satoshi

By Dennis Gannon, Marcus Christie, Suresh Marru, Satoshi Shirasuna, Aleksander Slominski (auth.), Patrick W. Gaffney, James C. T. Pool (eds.)

International Federation for info Processing

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Extra info for Grid-Based Problem Solving Environments: IFIP TC2/ WG 2.5 Working Conference on Grid-Based Problem Solving Environments: Implications for Development and Deployment of Numerical Software July 17–21, 2006, Prescott, Arizona, USA

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This paper discusses the issues associated with practical automation of scientific processes and workflows and illustrates this through workflows developed using the Kepler framework and tools. 2 Workflows Workflow technologies have a long history in the databases and information systems communities [1]. Scientific community has developed a number of problem-solving environments, most of them as integrated solutions [24 and references there in]. , 14, 25, 26, 29, 30]. , 2 – 11, 46, 55, 61]. 4 40 Vouk et al.

The goal of the VGrADS Project is to make Grid programming easier rather than more difficult, so requiring that the developer construct performance models by hand is out of the question. To address this issue, VGrADS researchers are exploring new methods for automatic construction of accurate performance models. This work, which produces remarkably accurate models for uniprocessor performance will be discussed in Section 3. 22 Grid-Based Problem Solving Environments Since the scheduling problem for DAGs is NP-complete, VGrADS uses heuristics to schedule workflows onto virtual grids.

Chiu. EMAN: Semiautomated software for high resolution single-particle reconstructions. J. Struct. Biol, (128):82–97, 1999. 9. A. Mandal, K. Kennedy, C. Koelbel, G. Marin, J. Mellor-Crummey, B. Liu, and L. Johnsson. Scheduling strategies for mapping application workflows onto the grid. In 14-th IEEE Symposium on High Performance Distributed Computing (HPDC14), pages 125–134, 2005. Grid-Based Problem Solving Environments 29 10. Gabriel Marin and John Mellor-Crummey. Cross architecture performance predictions for scientific applications using parameterized models.

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