A grid workflow-based Monte Carlo simulation environment

  • Authors:

  • Yaohang Li;Michael Mascagni;Robert van Engelen;Qin Cai

  • Affiliations:

  • Department of Computer Science, North Carolina A&T State University, Greensboro, NC;Department of Computer Science and School of Computational Science and Information Technology, Florida State University, Tallahassee, FL;Department of Computer Science and School of Computational Science and Information Technology, Florida State University, Tallahassee, FL;Department of Computer Science, Indiana University, Bloomington, IN

  • Venue:
  • Neural, Parallel & Scientific Computations - Special issue: Grid computing
  • Year:
  • 2004

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Abstract

Monte Carlo methods provide enormous scope for realistic statistical modeling and simulation. Implementation of large-scale Monte Carlo applications on the grid benefits from state-of-the-art approaches to accessing resources in a computational grid. Workflow techniques allow one to describe and enact his simulation processes in a structured, manageable, and verifiable way. We developed the Grid-Computing Infrastructure for Monte Carlo Applications (GCIMCA) based on the Globus toolkit and SPRNG library. The Globus toolkit facilitates creation and utilization of a computational grid for large distributed computational jobs and the Scalable Parallel Random Number Generators (SPRNG) library is designed to generate practically infinite number of random number streams with favorable statistical properties for distributed Monte Carlo applications. GCIMCA provides services specific to grid-based Monte Carlo simulation applications, including the Monte Carlo subtask schedule service using the N-out-of-M strategy, the facilities of application-level checkpointing, the partial result validation service, and the intermediate value validation service. Taking advantage of grid workflow paradigms and GCIMCA facilities, we implemented a Grid Workflow-based Monte Carlo (GWMC) simulation environment. Workflow management services are implemented to manage the Monte Carlo simulation process. We intend to provide a trustworthy and manageable grid-computing environment for large-scale and high-performance Monte Carlo simulation application.