Parallel Heuristics for an On-Line Scientific Database for Efficient Function Approximation (original) (raw)

Abstract

An effective approach for improving the efficiency of multi-scale combustion simulations is the use of on-line scientific databases. These databases allow for the approximation of computationally expensive functions by archiving previously computed exact values. A sequential software implementation of these database algorithms has proven to be extremely effective in decreasing the running time of complex reacting flow simulations. To enable the use of this approach on parallel computers, in this paper we introduce three heuristics for coordinating the distributed management of the database. We compare the performance of these heuristics on two limiting case test problems. These experiments demonstrate that a hybrid communication strategy offers the best promise for a large-scale, parallel implementation.

This work was supported by NSF grants CTS-0121573, ACI–9908057, and DGE–9987589 and ACI-0305743.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
    Ivana Veljkovic
  2. The Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
    Paul E. Plassmann

Authors

  1. Ivana Veljkovic
  2. Paul E. Plassmann

Editor information

Editors and Affiliations

  1. Computer Science Department, University of Tennessee, 37996-3450, Knoxville, TN, USA
    Jack Dongarra
  2. Department of Informatics and Mathematical Modelling, Technical University of Denmark, DK-2800, Lyngby, Denmark
    Kaj Madsen
  3. Informatics & Mathematical Modeling, Technical University of Denmark, DK-2800, Lyngby, Denmark
    Jerzy Waśniewski

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Veljkovic, I., Plassmann, P.E. (2006). Parallel Heuristics for an On-Line Scientific Database for Efficient Function Approximation. In: Dongarra, J., Madsen, K., Waśniewski, J. (eds) Applied Parallel Computing. State of the Art in Scientific Computing. PARA 2004. Lecture Notes in Computer Science, vol 3732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558958\_77

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