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Parallelisation of Wave Propagation Algorithms for Odour Propagation in Multi-Agent Systems

Parallelisation of Wave Propagation Algorithms for Odour Propagation in Multi-Agent Systems. Eugen Dedu , Supélec Stéphane Vialle , Supélec Claude Timsit , University of Versailles France. IWCC 2001, Septembre 1-6 Mangalia, Romania. Obstacle. Agent. Resource. Motivations and context.

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Parallelisation of Wave Propagation Algorithms for Odour Propagation in Multi-Agent Systems

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  1. Parallelisation of Wave Propagation Algorithms for Odour Propagation in Multi-Agent Systems Eugen Dedu, Supélec Stéphane Vialle, Supélec Claude Timsit, University of Versailles France IWCC 2001, Septembre 1-6 Mangalia, Romania

  2. Obstacle Agent Resource Motivations and context Large & distributed problem  too complex for total planning  distributed computing: sMAS & self organisation Example: carrying agents, obstacle avoiding, resources spread potential High execution times, especially for wave propagation  optimisation & parallelisation Global data access during the simulation  shared-memory most appropriate

  3. Several resources 1 2 1 2 3 2 1 1 1 1 1 2 3 4 3 2 2 3 2 1 2 1 1 1 5 1 3 1 2 3 4 3 2 2 4 4 2 3 2 3 6 5 3 1 5 5 2 4 4 3 4 3 4 3 2 2 1 1 4 1 5 5 5 4 4 3 1 5 1 2 3 4 3 4 6 3 3 2 3 4 5 5 2 1 1 5 4 1 2 1 2 3 4 3 3 2 1 1 2 3 2 1 5 4 4 3 1 1 2 1 2 1 2 3 2 2 3 1 1 1 2 1 2 3 2 1 1 2 1 1 p = max pi Wave propagation model 1 resource 6 p = pR - d Avoid obstacles Simple (& fast) hypotheses working in our AI simulations

  4. 1 1 2 1 1 2 3 1 2 1 1 2 3 3 2 3 1 1 2 2 2 1 1 1 Sequential, recursive method Depth-first Breadth-first 1 2 1 1 3 1 1 2 2 1 4 4 3 1 fine square update => fewer updates & overhead

  5. Sequential, iterative method • put potential of resources • repeat • for each square • p = max pi - 1 • until no modification systematic & simple => numerous updates & less overhead

  6. Iterative vs. recursive Recursive Iterative Number of obstacles numerousvery few Potential of resources smallhigh

  7. Parallel, domain decomposition Domain propagation Frontier exchange Frontier propagation Advantage: small data transfer Drawback: several repropagations

  8. P1 P2 P3 Parallel, private environments P1 P2 P3 Advantage: avoid repropagations Drawbacks: cache misses higher memory requirements

  9. Performance, execution time Recursive domain Iterative domain Recursive private Obstacles 0% Resources 1% Potential 16 SMP, Sparc, 4 processors

  10. Performance, execution time Recursive domain Iterative domain Recursive private Obstacles 16% Resources 1% Potential 8 DSM, Origin2000, 64 processors

  11. Performance, execution time Recursive domain Iterative domain Recursive private Obstacles 0% Resources 1% Potential 16 DSM, Origin2000, 64 processors

  12. Performance, theoretical speed-up Use iterative method for domain propagation and recursive method for frontier propagation “User point of view”

  13. Future directions • Current results • Sequential methods: • Recursive - fine-grained & needed updates, overhead • Iterative - systematic & simple, less overhead • Parallelisation methods: • Frontier exchanges - no data transfer, repropagations • Private environments - data miss, avoid repropagation Future research Implement & evaluate other sequential and parallel methods Measure performance on clusters offering shared-memory semantic

  14. Final goal... distributed memory Parallel programming paradigm chosen: shared-memory programming (easier) Shared-memory architectures Distributed shared-memory architectures Cluster of workstations... - cheap (expected) - user can upgrade frequently  always better than best sequential machine End user

  15. Questions...

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