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Distributed Load Balancing for Parallel Agent-based Simulations

This paper discusses methods for enhancing parallel agent-based simulations by implementing effective load balancing strategies. Building on Reynolds' model, we explore agent partitioning and demonstrate a new lightweight schema applicable to clusters of workstations. We evaluate the performance with various agent distributions and balancing strategies (static/dynamic). Our experiments, conducted on an IBM HS21 Cluster, show promising scalability up to one million agents, addressing the complexities of maintaining performance amid dynamic agent interactions. Future work aims for scaling beyond one billion agents.

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Distributed Load Balancing for Parallel Agent-based Simulations

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  1. Distributed Load Balancing for Parallel Agent-based Simulations Biagio Cosenza* , Gennaro Cordasco, Rosario De Chiara, Vittorio ScaranoISISLab, Dipartimento di Informatica Università degli Studi di Salerno, Italy

  2. Outline • Agent-based Simulations • Reynolds’ model • Agents partitioning • Load balancing with two processors • Generalization to multiple workers • Results

  3. Agent-based Simulations • Simulate the actions and interactions of autonomous individual agents with a view to assessing their effects on the system as a whole • Investigated since the 1980s • Early works take inspiration from particles [R83] • Applications • Biology, economics, sociology … • Whyparallel? • Hugenumberofagents • Complexbehaviours Social interactions Aging Fly around Death Birth

  4. Reynolds’ Model • Alignment: steer towards the average heading of local flock-mates • Cohesion: steer to move toward the average position of local flock-mates • Separation: steer to avoid crowding local flock-mates Alignment Cohesion Separation Alignment Cohesion Separation

  5. ParallelizationofAgent-basedSimulation • SpacePartitioning • Distributed (notcentralized) • Evendistribution (loadbalancing) • Target architectureis a cluster ofworkstations

  6. ParallelizationofAgent-basedSimulation The simulation carried out on 4 workers

  7. Agent-based Distribution sparse distribution dense distribution

  8. Loadbalancingwithtwoprocessors

  9. Two Processors Schema • Assumptions • the measure of the computational load of each worker is linear in the number of agents • agents are uniformly distributed along the dimension where the splittingoccurs

  10. Two-procs SchemaRegion wide loadbalancing (Dynamic1) Assumption: agents uniformly distributed slice of space containing a single agent, under the uniformly distributed agents assumption number of agents to be moved to balance the load between the left and right processor Remarks: sr (sl ) is the number of agents in Sr (Sl)

  11. The Oscillation Problem • Agents are usually more dense on the boundary • The oscillation is an effect of the assumption of uniformity of distribution, that underestimates the amount of agents approaching the boundary • When a large flock of agents rapidly moves between the two workers • the boundary is moved too aggressively in the attempt of balancing the flock movement and it oscillates

  12. Two-procs Schema Mitigated region wide load balancing (Dynamic2) Remarks: sr(sl) is the number of agents in Sr (Sl) Introducing a mitigation factor k slice of space containing a single agent, under the uniformly distributed agents assumption number of agents to be moved to balance the load between the left and right processor mitigation factor (we used k=0.5)

  13. Two-procs SchemaRestricted assumption loadbalancing (Dynamic 3) Remarks: εis the radiusofagents’ AOI sr(sl) is the number of agents in Sr (Sl) er(el ) is the number of agents in Er (El) • Relaxed assumption of uniformity of the distribution of the agents to the spaces El and Er, instead of Sl and Sr • Limit the per-step movement of the boundary to ε

  14. Generalization to Multiple Workers • We apply the 2-workers schema to couples of neighbors workers, on alternate steps

  15. Test Settings • Hardware • IBM HS21 Cluster available at ENEA CRESCO Project, Portici (Italy) • Each node: 2 Xeon QuadCoreClovertown E5345 at 2.33 GHz, 16 GByte RAM • Infiniband network • Software Implementation • OpenSteer, C++, Intel compiler, Message passing (MPI) • The set of tests we performed is the result of the Cartesian product: {1,000, 10,000, 100,000} × numberofagents{8, 16, 32, 64} × numberofprocessors{static, dynamic1, dynamic2, dynamic3} loadbalancingstrategy

  16. Results Distribution of agents per worker Each color represents a different worker. x-axis indicates the simulation step (1, 000 simulation steps are depicted) while th y-axis represents the distribution of agents (a) (b) (d) (c) (a) (100,000; 8; static) (d) (100, 000; 8; dynamic3) (b) (100, 000; 8; dynamic1) (c) (100, 000; 8; dynamic2)

  17. ResultsScalability number of steps per second number of procs A comparison between (100,000;*; static) (dotted line) and (100,000;*; dynamic3) (continuous line)

  18. Agents’ Partitioning Demo Video

  19. Conclusion • Agent-basedSimulation and ParallelComputing • Wellsuited • Loadbalancingischallenging • Weintroduced a newloadbalancing schema • Lightweight • Distributed • Goodscalability • Up to 1,000,000 agents • Nextsteps • 1,000,000,000 • Complexbehaviours

  20. Thanks for your attention Questions? http://www.isislab.it/projects/DistrSteer/

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