Switching to High Gear: Opportunities for Grand-Scale Real-Time Parallel Simulations

The recent emergence of dramatically large computational power, spanning desktops with multi-core processors and multiple graphics cards to supercomputers with 105 processor cores, has suddenly resulted in simulation-based solutions trailing behind in the ability to fully tap the new computational capacity. Here, we motivate the need for switching the parallel simulation research to a higher gear to exploit the new, immense levels of computational power. The potential for grand-scale real-time solutions is illustrated using preliminary results from prototypes in four example application areas: (a) state- or regional-scale vehicular mobility modeling, (b) very large-scale epidemic modeling, (c) modeling the propagation of wireless network signals in very large, cluttered terrains, and, (d) country- or world-scale social behavioral modeling. We believe the stage is perfectly poised for the parallel/distributed simulation community to envision and formulate similar grand-scale, real-time simulation-based solutions in many application areas.

[1]  C. Macken,et al.  Modeling targeted layered containment of an influenza pandemic in the United States , 2008, Proceedings of the National Academy of Sciences.

[2]  Jens H. Krüger,et al.  A Survey of General‐Purpose Computation on Graphics Hardware , 2007, Eurographics.

[3]  J. S. Chen,et al.  Efficient indoor and outdoor EM wave propagation in a compact terrain database (CTDB) representation of the urban canyon environment , 2002, Proceedings IEEE 56th Vehicular Technology Conference.

[4]  Joshua M Epstein,et al.  Modeling civil violence: An agent-based computational approach , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Ernest H. Page,et al.  Beyond speedup: PADS, the IILA and web-based simulation , 1999, Proceedings Thirteenth Workshop on Parallel and Distributed Simulation. PADS 99. (Cat. No.PR00155).

[6]  Madhav V. Marathe,et al.  EpiSimdemics: An efficient algorithm for simulating the spread of infectious disease over large realistic social networks , 2008, 2008 SC - International Conference for High Performance Computing, Networking, Storage and Analysis.

[7]  Christopher D. Carothers,et al.  Analysis of time warp on a 32,768 processor ibm blue Gene/L supercomputer , 2008 .

[8]  Michael J. North,et al.  Experiences creating three implementations of the repast agent modeling toolkit , 2006, TOMC.

[9]  Christopher D. Carothers,et al.  Scalable Time Warp on Blue Gene Supercomputers , 2009, 2009 ACM/IEEE/SCS 23rd Workshop on Principles of Advanced and Distributed Simulation.

[10]  Chung-Yuan Huang,et al.  Learning to build network-oriented epidemic simulation models in epidemiology education , 2008, Int. J. Simul. Process. Model..

[11]  Kalyan S. Perumalla,et al.  Computational Spectrum of Agent Model Simulation , 2010 .

[12]  Matt Pharr,et al.  Gpu gems 2: programming techniques for high-performance graphics and general-purpose computation , 2005 .

[13]  Jon Parker A flexible, large-scale, distributed agent based epidemic model , 2007, 2007 Winter Simulation Conference.

[14]  Dawid Pajak General-Purpose Computation Using Graphics Hardware for Fast HDR Image Processing , 2007 .

[15]  Joshua M. Epstein,et al.  Modeling civil violence: An agent-based computational approach , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Sudip K. Seal,et al.  Parallel Agent-Based Simulations on Clusters of GPUs and Multi-Core Processors , 2010 .

[17]  Aravind Srinivasan,et al.  Modelling disease outbreaks in realistic urban social networks , 2004, Nature.

[18]  Boleslaw K. Szymanski,et al.  Simulating Spatially Explicit Problems on High Performance Architectures , 2002, J. Parallel Distributed Comput..

[19]  Eileen Kraemer,et al.  SASSY: A Design for a Scalable Agent-Based Simulation System using a Distributed Discrete Event Infrastructure , 2006, Proceedings of the 2006 Winter Simulation Conference.

[20]  Daryl J. Daley,et al.  Epidemic Modelling: An Introduction , 1999 .

[21]  V. Protopopescu,et al.  An Event Driven, Simplified TLM Method for Predicting Path-Loss in Cluttered Environments , 2008, IEEE Transactions on Antennas and Propagation.

[22]  Kalyan S. Perumalla Scaling time warp-based discrete event execution to 104 processors on a Blue Gene supercomputer , 2007, CF '07.

[23]  Charles M. Macal,et al.  Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation , 2007 .

[24]  Kalyan S. Perumalla,et al.  /spl mu/sik - a micro-kernel for parallel/distributed simulation systems , 2005, Workshop on Principles of Advanced and Distributed Simulation (PADS'05).

[25]  Stefan Bilbao,et al.  Wave and Scattering Methods for Numerical Simulation , 2004 .

[26]  Charles M. Macal,et al.  Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation , 2007 .

[27]  Christopher N. Eichelberger,et al.  Actionable Capability for Social and Economic Systems (ACSES) , 2008 .

[28]  Sudip K. Seal,et al.  GPU-based Real-Time Execution of Vehicular Mobility Models in Large-Scale Road Network Scenarios , 2009, 2009 ACM/IEEE/SCS 23rd Workshop on Principles of Advanced and Distributed Simulation.

[29]  Lee D. Han,et al.  A METHODOLOGY FOR THE ASSESSMENT OF TRAFFIC MANAGEMENT STRATEGIES FOR LARGE-SCALE EMERGENCY EVACUATIONS , 2001 .

[30]  Chung-Yuan Huang,et al.  Influences of Resource Limitations and Transmission Costs on Epidemic Simulations and Critical Thresholds in Scale-Free Networks , 2009, Simul..

[31]  Dhananjai Madhava Rao,et al.  Parallel simulation of the global epidemiology of Avian Influenza , 2008, 2008 Winter Simulation Conference.

[32]  Kalyan S. Perumalla,et al.  A Connectionist Modeling Approach to Rapid Analysis of Emergent Social Cognition Properties in Large-Populations , 2009 .

[33]  Sudip K. Seal,et al.  Scalable Parallel Execution of an Event-Based Radio Signal Propagation Model for Cluttered 3D Terrains , 2009, 2009 International Conference on Parallel Processing.

[34]  David W. Bauer,et al.  Optimistic parallel discrete event simulation of the event-based transmission line matrix method , 2007, 2007 Winter Simulation Conference.

[35]  H. R. Anderson,et al.  A comparative study of ray tracing and FDTD for indoor propagation modeling , 1998, VTC '98. 48th IEEE Vehicular Technology Conference. Pathway to Global Wireless Revolution (Cat. No.98CH36151).

[36]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .