MASS: A Parallelizing Library for Multi-Agent Spatial Simulation

Synopsis:

For more than the last two decades, multi-agent simulations have been highlighted to model mega-scale social or biological agents and to simulate their emergent collective behavior that may be difficult only with mathematical and macroscopic approaches. A successful key for simulating megascale agents is to speed up the execution with parallelization. Although many parallelization attempts have been made to multiagent simulations, most work has been done on shared-memory programming environments such as OpenMP, CUDA, and Global Array, or still has left several programming problems specific to distributed-memory systems, such as machine unawareness, ghost space management, and cross-processor agent management (including migration, propagation, and termination). To address these parallelization challenges, we have been developing MASS, a new parallel-computing library for multi-agent and spatial simulation over a cluster of computing nodes. MASS composes a user application of distributed arrays and multi-agents, each representing an individual simulation place or an active entity. All computation is enclosed in each array element or agent; all communication is scheduled as periodic data exchanges among those entities, using machine-independent identifiers; and agents migrate to a remote array element for rendezvousing with each other. Our unique agent-based approach takes advantage of these merits for parallelizing big data analysis using climate change and biological network motif searches as well as individual-based simulation such as neural network simulation and influenza epidemic simulation as practical application examples.

Group Photos:

2019 Lab
Summer 2012 Lab Members
2019 Lab
Summer 2013 Lab Members
2019 Lab
Autumn 2014 Lab Members
2019 Lab
Winter 2016 UWB-Shizuoka Research Workshop
2019 Lab
Winter 2017 Lab Members
2019 Lab
Winter 2018 Lab Members
2019 Lab
Autumn 2019 Lab Members
2020 Lab
Autumn 2020 Lab Members

Manuals:

  1. MASS Java: Parallel-Computing Library for Multi-Agent Spatial Simulation in Java, May 7th, 2010
  2. MASS C++: Parallel-Computing Library for Multi-Agent Spatial Simulation in C++, February 24th, 2015
  3. MASS Cuda: Parallel-Computing Library for Multi-Agent Spatial Simulation in CUDA, March 23rd, 2014
  4. MASS Debugger (C++): Debugger for Parallel-Computing Library for Multi-Agent Spatial Simulation in C++, December, 2015
  5. MASS Debugger (Java): Debugger for Parallel-Computing Library for Multi-Agent Spatial Simulation in Java, December, 2015

Software Downloading and Development:

MASS Java and C++ versions are now available to the public!
  1. MASS Developers Quick Reference Guide
  2. MASS C++ Developers Guide
  3. MASS Java Developer Guide
  4. MASS Java AWS Setup Guide
  5. Bitbucket for MASS Library Developers (Java, C++, and CUDA)
  6. Bitbucket for MASS Application Developers (Java, C++, and CUDA)

Publications:

  1. Matthew Sell, Munehiro Fukuda, Agent Programmability Enhancement for Rambling over a Scientific Dataset, to appear in PAAMS 2020, October 7-9, L'Aquila, Italy
  2. Munehiro Fukuda, Collin Gordon, Utku Mert, Matthew Sell, An Agent-Based Computational Framework for Distributed Data Analysis, In Computer, vol. 53, no. 3, pp. 16-25, March 2020.
  3. Lisa Kosiachenko, Nate Hart, Munehiro Fukuda, MASS CUDA: A General GPU Parallelization Framework for Agent-Based Models, International Conference of PAAMS 2019, Proc. of Advances in Practical Applications of Survivable Agents and Multi-Agent Systems, Avila, Spain, pages 139-152, June 2019
  4. Collin Gordon, Utku Mert, Matthew Sell, Munehiro Fukuda, Implementation Techniques to Parallelize Agent-Based Graph Analysis, International Workshops of PAAMS 2019, Proc. of Highlights of Practical Applications of Survivable Agents and Multi-Agent Systems, Avila, Spain, pages 3-14, June 2019
  5. Yun-Ming Shih, Collin Gordon, Munehiro Fukuda, Jasper van de Ven, Christian Freksa, Translation of String-and-Pin-Based Shortest Path Construction into Data-Scalable Agent-Based Computational Models, Proc. of the 2018 Winter Simulation Conference, pages 881-892, Gothenburg, Sweden, December 2018
  6. Jasper ven de Van, Munehiro Fukuda, Holger Schultheis, Christian Freksa, Thomas Barkowsky, Analyzing Strong Spatial Cognition: A Modeling Approach, Proc. Of 11th Int’l Conf. on Spatial Cognition 2018, Tubingen, Germany, pages 197-208, September 2018
  7. Collin Gordon, Munehiro Fukuda, Analysis of Agent-Based Parallelism for Use in Clustering and Classification, Int’l Conf of PAAMS 2018, Advances in Practical Applications of Agents, Multi-Agent Systems, and Complexity, pages 314-317, Toledo, Spain, June 2018
  8. Craig Shih, Caleb Yang, Munehiro Fukuda, Benchmarking the Agent Descriptivity of Parallel Multi-Agent Simulators, Int’l Workshops of PAAMS 2018, Highlights of Practical Application of Agents, Multi-Agent Systems, and Complexity, pages 480-492, Toledo, Spain, June 2018
  9. Delmar Davis, Jonathan Featherston, Hoa Vo, Munehiro Fukuda, Hazeline Asuncion, Data Provenance for Agent-Based Models in a Distributed Memory, Informatics Vol.5 No.2, doi:10.3390/informatics5020018, April 2018
  10. Delmar Davis, Jonathan Featherston, Munehiro Fukuda, Hazeline Asuncion, Data Provenance for Multi-Agent Models, In Proc. of the 13th IEEE International eScience Conference, 10.1109/eScience.2017.16, Auckland, New Zealand, October 24-27, 2017
  11. Christopher Bowzer, Benjamin Phan, Kasey Cohen, Munehrio Fukuda, Collision-Free Agent Migration in Spatial Simulation, In Proc. of the 11th International Workshop on Multi-Agent Systems and Simulation - MAS&S'17, Prague, Czech Republic, September 3-6, 2017
  12. Jason Woodring, Matthew Sell, Munehiro Fukuda, Hazeline Asuncion, Eric Salathe, A Multi-Agent Parallel Approach to Analyzing Large Climate Data Sets, The 37th IEEE International Conference on Distributed Computing Systems, pages 1639-1648, Atlanta, GA, June 5-8, 2017
  13. Andrew Andersen, Wooyoung Kim, Munehiro Fukuda, MASS-based NemoProfile Construction for an Efficient Network Motif Search, Big Data and Cloud Computing in Bioinformatics - BDCloud 2016 October 8-10, 2016
  14. Zhiyuan Ma, Munehiro Fukuda, A Multi-Agent Spatial Simulation Library for Parallelizing Transport Simulation, the 2015 Winter Simulation Conference - WSC 2015, Newport Beach, December 6-9, 2015
  15. Bhargav Mistry, Munehiro Fukuda Dynamic Load Balancing in Multi-Agent Spatial Simulation , PacRim 2015, Victoria, B.C., August 24-26, 2015, (Best Paper Award)
  16. Matthew Kipps, Wooyoung Kim, Munehiro Fukuda Agent and Spatial Based Parallelization of Biological Network Motif Search, 17th IEEE International Conference on High Performance Computing and Communications - HPCC 2015, New York, August 24-26, 2015 (PPT file)
  17. Brett Yasutake, Niko Simonson, Jason Woodring, Nathan Duncan, William Pfeffer, Hazeline Asuncion, Munehiro Fukuda, Eric Salathe, Supporting Provenance in Climate Science Research, In Proc. of 7th International Conference on Information, Process, and Knowledge Management, eKnow 2015, Lisbon, Portugal, February 22-27, 2015 (Best Paper Award)
  18. Timothy Chuang, Munehiro Fukuda, A Parallel Multi-Agent Spatial Simulation Environment for Cluster Systems, In Proc. of the 16th IEEE International Conference on Computational Science and Engineering - CSE 2013, pages 143-150, Sydney, Australia, December 3-5, 2013
  19. Niko Simonson, Sean Wessels, Munehiro Fukuda, Language and Debugging Support for Multi-Agent and Spatial Simulation In Proc. of the 2012 International Conference on Parallel and Distributed Processing Techniques and Applications - PDPTA 2012, pages 373-379, Las Vegas, July 16 - 18, 2012
  20. Elad Mazurek, Munehiro Fukuda, A Parallelization of Orchard Temperature Predicting Programs In Proc. of 2011 IEEE Pacific Rim Conference on Communications, Computers and Signal Prcessing - PACRIM'11, pages 179-184, Victoria, BC, Canada, August 24-26, 2011
  21. John Emau, Timothy Chuang, Munehiro Fukuda, A Multi-Process Library for Multi-Agent and Spatial Simulation. In Proc. of 2011 IEEE Pacific Rim Conference on Communications, Computers and Signal Prcessing - PACRIM'11, pages 369-376, Victoria, BC, Canada, August 24-26, 2011

Presentation Slides:

  1. Agent-Based Computing, CSS599 Faculty Research Seminar, November 13, 2019
  2. UWB-Shizuoka University Research Workshop, March 2, 2016
  3. A Parallel Multi-Agent Spatial Simulation Environment for Cluster Systems (18 pages), the 16th IEEE International Conference on Computational Science and Engineering, Sydney, Australia, December 4, 2013
  4. MASS: A Multi-Agent Spatial Simulation Library (a one-Page flier), Office of Research - Undergraduate Research Fair, University of Washington Bothell, October 15, 2013
  5. MASS: A Multi-Agent Spatial Simulation Library (26 pages), Office of Research - Research in Progress, University of Washington, Bothell, April 23, 2013

Members: