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Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations
Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations
Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations
Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations
Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations
Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations
Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations
Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations
Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations
Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations
Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations
Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations
Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations
Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations
Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations
Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations
Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations
Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations
Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations
Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations
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Scalable Agent-based Modelling with Cloud HPC Resources for Social Simulations

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New concepts like agent-based modelling are providing social scientists with new tools, more suited to their background than other simulation techniques. The success of this new trend will be strongly …

New concepts like agent-based modelling are providing social scientists with new tools, more suited to their background than other simulation techniques. The success of this new trend will be strongly related to the existence of simulation tools capable of fulfilling the needs of these disciplines. Given the computational requirement of realistic agent-based models, high-performance computing infrastructure is often necessary to perform the calculations. At present, such resources are unlikely to be available to humanities researchers. Having developed Pandora, an open-source framework designed to create and execute large-scale social simulations in high-performance computing environments, this work presents an evaluation of the impact of cloud computing within this context. We find that the constraints of the cloud environment do not have a significant impact on the generic pattern of execution, providing a cost-effective solution for social scientists.

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  • 1. Scalable Agent-based Modelling with Cloud HPC Resources for Social SimulationsPeter Wittek – University of BorasXavier Rubio-Campillo – Barcelona Supercomputing Centre 1
  • 2. Introduction Computer simulation is increasingly used as a research tool in Humanities and Social Sciences projects ABM is one the most promising tools, but its computing requirements can be high HPC is the answer to this issue, but there are few applications of social simulation in these environments... Why? What role plays Cloud Computing in the solution?
  • 3. Agent-Based Modelling in Social Sciences Inspired on SugarScape model Two key concepts: Agent → individual entity Environment → cellular automata The agent is defined by: Attributes defining its internal state A “step” method containing predefined rules of behavior
  • 4. ABM benefits & issues ABM is attractive to social scientists because: Bottom-up approach to social phenomena Heterogeneity and environment Advanced decision-making processes Closer to social science & humanities thinking But we need to address some problems: Wrong understanding of what a model is (and is not) How to develop and understand a piece of software? How to analyze and share results?
  • 5. ABM computational challenges How can we distribute an execution in an efficient way? Is possible to use more than 1 CPU/node? How to serialize a constant rate of data without bottlenecks?
  • 6. An example: distributing past societies The requirements are diverse, given the broad scope of ABM We need to address these challenges for different scenarios i.e.: to model past societies we should take into account these properties: Importance of environment Low number of agents Intensive local communication How can we use this information to distribute the ABM?
  • 7. The Pandora framework Pandora is an open-source C++ framework developed to accomplish this task. Implementation & execution: Automatic generation of code for parallel execution Python interface Distributed serialization (HDF5) Analysis: GIS support (GRASS) & Statistical support (R package) Cassandra: an ABM visualization tool
  • 8. Pandora workflow
  • 9. Distributing the simulation Order of execution
  • 10. Breaking sequentiality From: step() To: updateKnowledge() selectAction() execute() updateState()
  • 11. ABM in the cloud HPC-CC is an interesting option because: ABM requires considerable computational power Several Humanities/Social Sciences departments do not have access to HPC resources It is difficult to share data ...but how this solution can impact the performance of an ABM framework like Pandora?
  • 12. The experimental model We will explore this problem with a model currently in use for archaeologists: gujaratSim This ABM explores interactions between 2 populations competing for resources in a common environment: hunter/gatherers Pastoralists Some details of gujaratSim are: Advanced decision making process (Markov Decision Process) Intensive use of enviromental data (biomass, water, etc.) Exploration of different population sizes
  • 13. Infrastructures The experiment will test 2 CC environments and a supercomputing infrastructure: CC → Amazon Web Services EC2-regular EC2-cluster EC2-cluster unthrottled Supercomputer → Mare Nostrum Each node has 2 dual core IBM PowerPc 970 MP at 2.3 GhZ Myrinet high-speed network
  • 14. Performance experiments We will test 2 different configurations with 2x2 and 3x3 nodes CPU intensive Low number of agents Intensive MDP algorithm Communication intensive High number of agents Wired (rule-based) agent behavior
  • 15. CPU intensive benchmarkParallel efficiency Load balance
  • 16. CPU intensive MPI trace
  • 17. Communication intensive benchmark Parallel efficiency Load balance
  • 18. Communication intensive MPI trace
  • 19. Conclusions ABM is one of the most promising computational tools for investigating social phenomena We need to fulfill its computational requirements using HPC Cloud-based HPC and multiplatform software are needed for this task ...can we develop GPU-based solutions? ...can we lower technical knowledge needed to develop distributed ABMs?
  • 20. Thank you! There is a need for cloud HPC in social simulation Pandora allows to deploy ABM in a cloud-based solution Supercomputers and Cloud-HPC are complementary, given the diversity of problems to solve xavier.rubio@bsc.es If you are interested you can download Pandora from: http://github.com/xrubio/pandora 20

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