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High Performance Geo-Simulations

This paper explores challenges in geo-simulations, emphasizing the need for effective programming expertise among modelers. It discusses key concepts such as process partitioning, communication, and synchronization within simulations, and the trade-offs between scalability and performance. The methodology includes defining model architecture based on application domains and modeling paradigms, and implementing dynamic load-balancing strategies. Two experiments demonstrate the efficiency of parallel execution using different models. Results indicate significant improvements in simulation speed with appropriate parallelization techniques.

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High Performance Geo-Simulations

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  1. High Performance Geo-Simulations Saulo Henrique Cabral Silva Tiago Garcia de Senna Carneiro* TerraLAB – Laboratory for Earh System ModelingandSimulation ComputerScienceDepartment Federal Universityof Ouro Preto GeoInfo 2013

  2. Challenges • Modelers have few programming expertise • Problem partition into several processes? • Processes communication? • Processes synchronization? • What is the design goal: Scalability or Performance? • Global scheduling  scalabitity • Local scheduling  high performance • How to parallelize/distribute workload? • Model architecture can be defined by: • Application domain area (the phenomenon being modeled) • Chosen modeling paradigm: CA, multiagente, DEVS, etc • Which load balancing strategy to use? • Agents clusters • Dynamic and adaptative • How to verify models with no influence of concurrency control rules?

  3. Flexibility through annotation: Prey-predator model Annotations - API: HPA PARALLEL HPA JOINfunction_name HPA JOINALL HPA ACQUIREresource_name HPA RELEASEresource_name function execAgentPred(i) predatores[i]:execute() end function execAgentPrey(i) preys[i]:execute() end for i = 1,#predatores do --HPA PARALLEL execAgentPred(i) end --HPA JOINALL for i = 1,#preys do --HPA PARALLEL execAgentPrey(i) end --HPA JOINALL

  4. Scalability through load balance: Bag of Tasks

  5. Discussion • TerraME HPA scales well when the modeler designs efficient parallel solutions • 90% of linear speedup with 8 processors (prey predator model) • 35% of linear speedup with 8 processors (TROLL model)

  6. EXP1: Spatially-explicit prey predator model • Species compete, evolve and disperse, in order to seek resources to support their struggle for their existence • Each cell has a population of prey and a population of predators • Model executes in two steps: • First, predators are simulated in parallel • Then, prey are simulated in parallel

  7. Distributed(Dmason)versusParallel(TerraME)

  8. Agents Clusters versusLoadBalancing

  9. EXP2: 3D tropical rain forest growth model - TROLL • TROLL model simulates a 3D environment where: • Individual trees compete for light • Seed dispersal by wind • Trees interacts falling over others and forming gaps • … • Adopted partitioning strategy: each layer (h, h-1, h-2, …) is simulated in parallel • Higher layers are simulated before lower layers

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