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Almansur : Extendable I mplementation and P arallelization Using MASS

Almansur : Extendable I mplementation and P arallelization Using MASS. Stuart Drummond. Introduction. Almansur Overview. Massively multiplayer online turn-based strategy game Built using Ruby on Rails by PDMFC Originally released in 2007 Created using agent-based modeling.

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Almansur : Extendable I mplementation and P arallelization Using MASS

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  1. Almansur: Extendable Implementation and Parallelization Using MASS Stuart Drummond

  2. Introduction

  3. Almansur Overview • Massively multiplayer online turn-based strategy game • Built using Ruby on Rails by PDMFC • Originally released in 2007 • Created using agent-based modeling Introduction Slide 1

  4. MASS • Created and maintained by Dr. Munehiro Fukuda and Distributed System Lab at UW Bothell • What is it? • Places & Agents • Using multiple computing nodes via grid computing to parallelize • Versions • Java and C++ • Which one is being used for the project? Introduction Slide 2

  5. Game parallelization & Benchmarking • Specific to games in web… • Most literature had to do parallelizing and scaling game server • AlBahnassi, Mudur, & Goswami, 2012 • Use of task parallelism • External validity in benchmarking Introduction Slide 3

  6. Agent-Based Systems • What are agents? • Macal& North (2008) • What are the features of agents? • Autonomy • Social relationships • Reactivity • Proactiveness • What kind of systems could benefit from agent-based systems? Not this kind of agents Introduction Slide 4

  7. Project Overview & Implementation

  8. The Problem and Requirements • Overall Speed • Ding, Gernart, Li & Hertz (2014) • Turn Processing • Functional Requirement • Performance • Non-functional Requirements • Extendibility Project Overview Slide 1

  9. Benchmarking Results (Ribeiro, Santos, & Prada 2007) Project Overview Slide 2

  10. Benchmarking Results (Ribeiro, Santos, & Prada 2007) Project Overview Slide 3

  11. Worst case scenario… Project Overview Slide 4

  12. Project Hypothesis • H0: There is no difference in performance between the original version of Almansur and the MASS-optimized Almansur. • H1: The amount of time that it takes to conduct turn processing will be significantly faster in the MASS-optimized Almansur. • Operational Definition • Speed: Less than 5% change in difference is considered within margin of error. Project Overview Slide 5

  13. Architecture Implementations Slide 6

  14. Components of Almansur Implementations Slide 7

  15. MASS-optimized Almansur Implementations Slide 8

  16. World.process_turn() Implementations Slide 9

  17. Event Manager • Class responsible for relaying events from turn processing to agents. • Reads from a JSON file and parsed by GSON. • Additional processing to determine if the target is a place or an agent. Implementations Slide 10

  18. Event Manager Continued… Implementations Slide 11

  19. JSON File Implementations Slide 12

  20. Source code documentations Implementations Slide 13

  21. Methods

  22. Performance Metrics • Ruby benchmarking modules • Using System.currentTimeMillis() at two points. • Graph + performance difference percentage table • Process 1 turn • PC used for testing: • CPU: i7 2600k @ 4.7 GHz • Memory: 8 GB RAM • Storage: Samsung 830 Pro SSD • GPU: 2x Nvidia GTX 780 TI Methods Slide 1

  23. Scenario Selection • Using the same set of scenarios used by the graduate student at PDMFC • Importing provided SQL dumps • Scenarios: • Small scenario with 2 Lands • Large scenario with 200 Lands • Very large scenario with 500 Lands • Huge scenario with 1000 Lands Methods Slide 2

  24. Results

  25. Scenario 1 Table (Milliseconds) Number of Lands: 2 Number of Agents: 511 Results Slide 1

  26. Scenario 2 Table (Milliseconds) Number of Lands: 200 Number of Agents: 28005 Results Slide 2

  27. Scenario 3 Table (Milliseconds) Number of Lands: 500 Number of Agents: 65420 Results Slide 3

  28. Scenario 4 Table (Milliseconds) Number of Lands: 1000 Number of Agents: 126424 Results Slide 4

  29. Performance Scatterplot Results Slide 5

  30. Conclusions

  31. Hypothesis Revisited • The implementation using MASS allowed for a processing time that was better than the original version • Was able to reject the null hypothesis • Performance bottleneck? • Any operation that involve having to refer back to the Lands / Political Entities create sections of code where parallelizable become impossible • Innate issue with the original design • Future implementation may start individualizing from the original software Conclusions Slide 1

  32. Limitations • No multi-process results • Low sample size • Repetitive importing code • Some processes are missing Conclusions Slide 2

  33. Future extensions • Web extension • Proper scenario importing and exporting • Additional processing • Further testing Conclusions Slide 3

  34. Lessons Learned • The requirements of a project changes…often • Having a library that can evolve is helpful for a project • Assuming anything about the source code is risky! • Communication is key • Porting from a dynamic-typed language to a static-typed language is difficult. Conclusions Slide 4

  35. References • Active record pattern. (2005, October 11). Retrieved September 1, 2014, from http://en.wikipedia.org/wiki/Active_record_pattern • Adobbati, R., Marshall, A. N., Scholer, A., Tejada, S., Kaminka, G. A., Schaffer, S., & Sollitto, C. (2001, January). Gamebots: A 3d virtual world test-bed for multi-agent research. In Proceedings of the second international workshop on Infrastructure for Agents, MAS, and Scalable MAS (Vol. 5). Montreal, Canada. • AlBahnassi, W., Mudur, S. P., & Goswami, D. (2012, June). A Design Pattern for Parallel Programming of Games. In High Performance Computing and Communication & 2012 IEEE 9th International Conference on Embedded Software and Systems (HPCC-ICESS), 2012 IEEE 14th International Conference on (pp. 1007-1014). IEEE. • Almansur. (2005). Free online medieval strategy turn based game. Retrieved May 1, 2014, from http://www.almansur.net/ • Barata, A. M., Santos, P. A., & Prada, R. (2011, September). AI for Massive Multiplayer Online Strategy Games. In AIIDE. • Chiu, A., Nasiri, E., & Rashid, R. (2012). Parallelization of Sudoku. • Ding, C., Gernhart, B., Li, P., & Hertz, M. (2014). Safe parallel programming in an interpreted language (Vol. 991). Technical Report. • Dong, J., Chen, S., & Jeng, J. J. (2005, April). Event-based blackboard architecture for multi-agent systems. In Information Technology: Coding and Computing, 2005. ITCC 2005. International Conference on (Vol. 2, pp. 379-384). IEEE. • Fukuda, M. (2014, January 30). MASS: A Parallelizing Library for Multi-Agent Spatial Simulation. MASS: A Parallelizing Library for Multi-Agent Spatial Simulation. Retrieved May 1, 2014, from http://depts.washington.edu/dslab/MASS/index.html • Jruby. (n.d.). Retrieved August 20, 2014. • Macal, C. M., & North, M. J. (2008, December). Agent-based modeling and simulation: ABMS examples. In Proceedings of the 40th Conference on Winter Simulation (pp. 101-112). Winter Simulation Conference. • Ribeiro, L. M. L., Santos, P., & Prada, R. (2007). Agents for Massive On-line Strategy Turn Based Games. • Ruby. (n.d.). Programming Language. Retrieved May 16, 2014, from https://www.ruby-lang.org/en • Ruby on Rails. (n.d.). Retrieved August 20, 2014, from http://rubyonrails.org/ • StarCraft II. (2010, January 1). Retrieved August 20, 2014, from http://us.battle.net/sc2/en/?-

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