Parallelisation of wave propagation algorithms for odour propagation in multi agent systems
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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 Propagation in Multi-Agent Systems

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 Propagation in Multi-Agent Systems

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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

1 Propagation in Multi-Agent Systems

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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 Propagation in Multi-Agent Systems

  • 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 Propagation in Multi-Agent Systems

Recursive Iterative

Number of obstacles numerousvery few

Potential of resources smallhigh


Parallel domain decomposition
Parallel, domain decomposition Propagation in Multi-Agent Systems

Domain

propagation

Frontier

exchange

Frontier

propagation

Advantage: small data transfer

Drawback: several repropagations


Parallel private environments

P1 Propagation in Multi-Agent Systems

P2

P3

Parallel, private environments

P1

P2

P3

Advantage: avoid repropagations

Drawbacks: cache misses

higher memory requirements


Performance execution time
Performance, execution time Propagation in Multi-Agent Systems

Recursive domain

Iterative domain

Recursive private

Obstacles 0%

Resources 1%

Potential 16

SMP, Sparc, 4 processors


Performance execution time1
Performance, execution time Propagation in Multi-Agent Systems

Recursive domain

Iterative domain

Recursive private

Obstacles 16%

Resources 1%

Potential 8

DSM, Origin2000, 64 processors


Performance execution time2
Performance, execution time Propagation in Multi-Agent Systems

Recursive domain

Iterative domain

Recursive private

Obstacles 0%

Resources 1%

Potential 16

DSM, Origin2000, 64 processors


Performance theoretical speed up
Performance, theoretical speed-up Propagation in Multi-Agent Systems

Use iterative method for domain propagation

and recursive method for frontier propagation

“User point

of view”


Future directions
Future directions Propagation in Multi-Agent Systems

  • 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 Propagation in Multi-Agent Systems

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


Questions
Questions... Propagation in Multi-Agent Systems


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