New challenges in dynamic load balancing

  • Authors:

  • Karen D. Devine;Erik G. Boman;Robert T. Heaphy;Bruce A. Hendrickson;James D. Teresco;Jamal Faik;Joseph E. Flaherty;Luis G. Gervasio

  • Affiliations:

  • Discrete Algorithms and Mathematics Department, Sandia National Laboratories, Albuquerque, NM, USA22Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, f ...;Discrete Algorithms and Mathematics Department, Sandia National Laboratories, Albuquerque, NM, USA22Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, f ...;Discrete Algorithms and Mathematics Department, Sandia National Laboratories, Albuquerque, NM, USA22Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, f ...;Discrete Algorithms and Mathematics Department, Sandia National Laboratories, Albuquerque, NM, USA22Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, f ...;Department of Computer Science, Williams College, Williamstown, MA, USA;Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA;Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA;Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA

  • Venue:
  • Applied Numerical Mathematics - Adaptive methods for partial differential equations and large-scale computation
  • Year:
  • 2005

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Abstract

Data partitioning and load balancing are important components of parallel computations. Many different partitioning strategies have been developed, with great effectiveness in parallel applications. But the load-balancing problem is not yet solved completely; new applications and architectures require new partitioning features. Existing algorithms must be enhanced to support more complex applications. New models are needed for non-square, non-symmetric, and highly connected systems arising from applications in biology, circuits, and materials simulations. Increased use of heterogeneous computing architectures requires partitioners that account for non-uniform computing, network, and memory resources. And, for greatest impact, these new capabilities must be delivered in toolkits that are robust, easy-to-use, and applicable to a wide range of applications. In this paper, we discuss our approaches to addressing these issues within the Zoltan Parallel Data Services toolkit.