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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

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

motivations and context
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

wave propagation model
Several resources

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p = max pi

Wave propagation model

1 resource

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p = pR - d

Avoid obstacles

Simple (& fast) hypotheses working in our AI simulations

sequential recursive method
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Sequential, recursive method

Depth-first

Breadth-first

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fine square update => fewer updates & overhead

sequential iterative method
Sequential, iterative method
  • put potential of resources
  • repeat
    • for each square
      • p = max pi - 1
  • until no modification

systematic & simple => numerous updates & less overhead

iterative vs recursive
Iterative vs. recursive

Recursive Iterative

Number of obstacles numerousvery few

Potential of resources smallhigh

parallel domain decomposition
Parallel, domain decomposition

Domain

propagation

Frontier

exchange

Frontier

propagation

Advantage: small data transfer

Drawback: several repropagations

parallel private environments
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P2

P3

Parallel, private environments

P1

P2

P3

Advantage: avoid repropagations

Drawbacks: cache misses

higher memory requirements

performance execution time
Performance, execution time

Recursive domain

Iterative domain

Recursive private

Obstacles 0%

Resources 1%

Potential 16

SMP, Sparc, 4 processors

performance execution time1
Performance, execution time

Recursive domain

Iterative domain

Recursive private

Obstacles 16%

Resources 1%

Potential 8

DSM, Origin2000, 64 processors

performance execution time2
Performance, execution time

Recursive domain

Iterative domain

Recursive private

Obstacles 0%

Resources 1%

Potential 16

DSM, Origin2000, 64 processors

performance theoretical speed up
Performance, theoretical speed-up

Use iterative method for domain propagation

and recursive method for frontier propagation

“User point

of view”

future directions
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

final goal distributed memory
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

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