An efficient 3D grid based scheduling for heterogeneous systems

  • Authors:

  • Anthony T. Chronopoulos;Daniel Grosu;Andrew M. Wissink;Manuel Benche;Jingyu Liu

  • Affiliations:

  • Department of Computer Science, University of Texas at San Antonio, 6900 N. Loop 1604 West, San Antonio, TX;Department of Computer Science, University of Texas at San Antonio, 6900 N. Loop 1604 West, San Antonio, TX;Center for Applied Scientific Computing, Lawrence Livermore National Lab, P.O. Box 808, L-661, Livermore, CA;Department of Computer Science, University of Texas at San Antonio, 6900 N. Loop 1604 West, San Antonio, TX;Department of Computer Science, University of Texas at San Antonio, 6900 N. Loop 1604 West, San Antonio, TX

  • Venue:
  • Journal of Parallel and Distributed Computing - Special section best papers from the 2002 international parallel and distributed processing symposium
  • Year:
  • 2003

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Abstract

The cost/performance ratio of networks of workstations has been constantly improving. This trend is expected to continue in the near future. The aggregate peak rate of such systems often matches or exceeds the peak rate offered by the fastest parallel computers. This has motivated research toward using a network of computers, interconnected via a fast network (cluster system) or a simple Local Area Network (LAN) (distributed system), for high performance concurrent computations. Some of the important research issues arise such as (i) Problem partitioning and virtual interconnection topology mapping; (ii) Execution scheduling and load balancing.Past results exist for grid partitioning (into subdomains) and mapping to parallel and distributed systems. In our work we consider the problem of grid partitioning of a 3D domain arising in aircraft CFD simulations in order to schedule tasks for load balanced execution on a heterogeneous distributed system. This problem has additional restrictions on how to partition the grid. Past work for this problem were on parallel systems with only few processor configurations. We derive heuristic algorithms for: (1) homogeneous systems with any number of processors; (2) heterogeneous systems taking into account the processor speed and memory capacity. We implement our algorithms on a dedicated network of workstations (using MPI) and test them with a CFD simulation code (TURNS--Transonic Unsteady Rotor Navier Stokes).