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

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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

6

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

RecursiveIterative

Number of obstaclesnumerousvery few

Potential of resourcessmallhigh


Parallel domain decomposition

Parallel, domain decomposition

Domain

propagation

Frontier

exchange

Frontier

propagation

Advantage: small data transfer

Drawback: several repropagations


Parallel private environments

P1

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


Questions

Questions...


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