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Time warp on the go

Authors:

Gabriele D'Angelo,

Stefano Ferretti,

Moreno MarzollaAuthors Info & Claims

SIMUTOOLS '12: Proceedings of the 5th International ICST Conference on Simulation Tools and Techniques

Pages 242 - 248

Published: 19 March 2012 Publication History

Abstract

In this paper we deal with the impact of multi and many-core processor architectures on simulation. Despite the fact that modern CPUs have an increasingly large number of cores, most softwares are still unable to take advantage of them. In the last years, many tools, programming languages and general methodologies have been proposed to help building scalable applications for multi-core architectures, but those solutions are somewhat limited. Parallel and distributed simulation is an interesting application area in which efficient and scalable multi-core implementations would be desirable. In this paper we investigate the use of the Go Programming Language to implement optimistic parallel simulations based on the Time Warp mechanism. Specifically, we describe the design, implementation and evaluation of a new parallel simulator. The scalability of the simulator is studied when in presence of a modern multi-core CPU and the effects of the Hyper-Threading technology on optimistic simulation are analyzed.

References

[1]

Parallel And Distributed Simulation (PADS) research group. http://pads.cs.unibo.it, 2011.

[2]

The Go Programming Language. http://golang.org/, 2012.

[3]

J. Armstrong. Programming Erlang: Software for a Concurrent World. Pragmatic Bookshelf, 2007.

Digital Library

[4]

L. Bononi, M. Bracuto, G. D'Angelo, and L. Donatiello. Exploring the effects of hyper-threading on parallel simulation. In Proceedings of the 10th IEEE international symposium on Distributed Simulation and Real-Time Applications, pages 257--260, Washington, DC, USA, 2006. IEEE Computer Society.

Digital Library

[5]

L. Dagum and R. Menon. Openmp: An industry-standard api for shared-memory programming. IEEE Comput. Sci. Eng., 5:46--55, January 1998.

Digital Library

[6]

G. D'Angelo. Parallel and distributed simulation from many cores to the public cloud. In HPCS '11: Proceedings of International Conference on High Performance Computing and Simulation. IEEE, 2011.

[7]

J. Dean and S. Ghemawat. Mapreduce: simplified data processing on large clusters. Commun. ACM, 51:107--113, Jan. 2008.

Digital Library

[8]

R. Ewald. Automatic Algorithm Selection for Complex Simulation Problems. Springer, 2011.

[9]

R. Fujimoto. Parallel and Distributed Simulation Systems. Wiley & Sons, 2000.

Digital Library

[10]

R. M. Fujimoto. Parallel discrete event simulation. In Proceedings of the 21st conference on Winter simulation, WSC '89, pages 19--28, New York, NY, USA, 1989. ACM.

Digital Library

[11]

R. M. Fujimoto. Performance of time warp under synthetic workloads. In Proceedings of the SCS Multiconference on Distributed Simulation, pages 23--28, 1990.

[12]

W. Gropp, E. Lusk, and A. Skjellum. Using MPI--Portable Parallel Programming with the Message Passing Interface. MIT Press, 2nd edition edition.

Digital Library

[13]

J. Himmelspach, R. Ewald, S. Leye, and A. M. Uhrmacher. Enhancing the scalability of simulations by embracing multiple levels of parallelization. In Proceedings of the 2010 Ninth International Workshop on Parallel and Distributed Methods in Verification, and Second International Workshop on High Performance Computational Systems Biology, PDMC-HIBI '10, pages 57--66, Washington, DC, USA, 2010. IEEE Computer Society.

Digital Library

[14]

D. Jefferson. Virtual time. ACM Transactions Program. Lang. Syst., 7(3):404--425, 1985.

Digital Library

[15]

D. W. Jones. An empirical comparison of priority-queue and event-set implementations. Commun. ACM, 29:300--311, April 1986.

Digital Library

[16]

Y. Jun, C. Raczy, and G. Tan. Evaluation of a sort-based matching algorithm for ddm. In Proceedings of the sixteenth workshop on Parallel and distributed simulation, PADS '02, pages 68--75, Washington, DC, USA, 2002. IEEE Computer Society.

Digital Library

[17]

Khronos OpenCL Working Group. The OpenCL Specification, version 1.1. http://www.khronos.org/opencl/, 2011.

[18]

L. Lamport. Time, clocks, and the ordering of events in a distributed system. Commun. ACM, 21:558--565, July 1978.

Digital Library

[19]

A. M. Law and D. M. Kelton. Simulation Modeling and Analysis. McGraw-Hill Higher Education, 3rd edition, 1999.

Digital Library

[20]

L. li Chen, Y. shuai Lu, Y. ping Yao, S. liang Peng, and L. da Wu. A well-balanced time warp system on multi-core environments. In Principles of Advanced and Distributed Simulation (PADS), 2011 IEEE Workshop on, pages 1--9, june 2011.

Digital Library

[21]

K. Manian and P. Wilsey. Distributed simulation on a many-core processor. In Proceedings of SIMUL 2011: Third Conference on Advances in System Simulation. IARIA, 2011.

[22]

D. T. Marr, F. Binns, D. L. Hill, G. Hinton, D. A. Koufaty, A. J. Miller, and M. Upton. Hyper-Threading Technology Architecture and Microarchitecture. Intel Technology Journal, 6(1), Feb. 2002.

[23]

F. Mattern. Efficient algorithms for distributed snapshots and global virtual time approximation. J. Parallel Distrib. Comput., 18:423--434, August 1993.

Digital Library

[24]

J. Misra. Distributed discrete event simulation. ACM Computing Surveys, 18(1):39--65, 1986.

Digital Library

[25]

J. Nickolls, I. Buck, M. Garland, and K. Skadron. Scalable parallel programming with cuda. Queue, 6:40--53, March 2008.

Digital Library

[26]

K. S. Perumalla. Switching to high gear: Opportunities for grand-scale real-time parallel simulations. Distributed Simulation and Real Time Applications, IEEE/ACM International Symposium on, 0:3--10, 2009.

Digital Library

[27]

C. Pheatt. Intel threading building blocks. J. Comput. Sci. Coll., 23:298--298, April 2008.

Digital Library

[28]

B. Samadi, R. Muntz, and D. Parker. A distributed algorithm to detect a global state of a distributed simulation system. In Prof. of the IFIP Conference on Distributed Processing, 1987.

[29]

N. Su, H. Hou, F. Yang, Q. Li, and W. Wang. Optimistic parallel discrete event simulation based on multi-core platform and its performance analysis. In Complex, Intelligent and Software Intensive Systems, 2009. CISIS '09. International Conference on, pages 675--680, march 2009.

[30]

S. Tay, G. Tan, and K. Shenoy. Piggy-backed time-stepped simulation with 'super-stepping'. In Simulation Conference, 2003. Proceedings of the 2003 Winter, volume 2, pages 1077--1085 vol.2, dec. 2003.

Digital Library

[31]

TILERA: TILE-Gx Processor Family. http://www.tilera.com/products/processors/TILE-Gx_Family, 2011.

[32]

X. Zeng, R. Bagrodia, and M. Gerla. Glomosim: a library for parallel simulation of large-scale wireless networks. SIGSIM Simul. Dig., 28(1):154--161, 1998.

Digital Library

Cited By

Eker AWilliams BChiu KPonomarev D(2019)Controlled Asynchronous GVTProceedings of the 48th International Conference on Parallel Processing10.1145/3337821.3337927(1-10)Online publication date: 5-Aug-2019
https://dl.acm.org/doi/10.1145/3337821.3337927
Eker AWilliams BMishra NThakur DChiu KPonomarev DAbu-Ghazaleh NRisco Martín JBesada EDe Rango F(2018)Performance implications of global virtual time algorithms on a knights landing processorProceedings of the 22nd International Symposium on Distributed Simulation and Real Time Applications10.5555/3330299.3330310(87-96)Online publication date: 15-Oct-2018
https://dl.acm.org/doi/10.5555/3330299.3330310
Kunz GStoffers MLandsiedel OWehrle KGross J(2016)Parallel Expanded Event Simulation of Tightly Coupled SystemsACM Transactions on Modeling and Computer Simulation (TOMACS)10.1145/283290926:2(1-26)Online publication date: 6-Jan-2016
https://dl.acm.org/doi/10.1145/2832909
Show More Cited By

Time warp on the go
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation types and techniques

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Information & Contributors

Information

Published In

cover image ACM Other conferences

SIMUTOOLS '12: Proceedings of the 5th International ICST Conference on Simulation Tools and Techniques

March 2012

402 pages

ISBN:9781450315104

General Chair:
George Riley
Georgia Institute of Technology
,
Program Chairs:
Francesco Quaglia
University of Rome "La Sapienza", Italy
,
Jan Himmelspach
University of Rostock, Germany

In-Cooperation

SIGSIM: ACM Special Interest Group on Simulation and Modeling

Publisher

ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)

Brussels, Belgium

Publication History

Published: 19 March 2012

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SIMUTOOLS'12

SIMUTOOLS'12: International ICST Conference on Simulation Tools and Techniques

March 19 - 23, 2012

Desenzano del Garda, Italy

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Overall Acceptance Rate 20 of 73 submissions, 27%

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Cited By

Eker AWilliams BChiu KPonomarev D(2019)Controlled Asynchronous GVTProceedings of the 48th International Conference on Parallel Processing10.1145/3337821.3337927(1-10)Online publication date: 5-Aug-2019
https://dl.acm.org/doi/10.1145/3337821.3337927
Eker AWilliams BMishra NThakur DChiu KPonomarev DAbu-Ghazaleh NRisco Martín JBesada EDe Rango F(2018)Performance implications of global virtual time algorithms on a knights landing processorProceedings of the 22nd International Symposium on Distributed Simulation and Real Time Applications10.5555/3330299.3330310(87-96)Online publication date: 15-Oct-2018
https://dl.acm.org/doi/10.5555/3330299.3330310
Kunz GStoffers MLandsiedel OWehrle KGross J(2016)Parallel Expanded Event Simulation of Tightly Coupled SystemsACM Transactions on Modeling and Computer Simulation (TOMACS)10.1145/283290926:2(1-26)Online publication date: 6-Jan-2016
https://dl.acm.org/doi/10.1145/2832909
Li ZCai WTurner SLi XDuong TGoh R(2015)Adaptive Resource Provisioning Mechanism in VEEs for Improving Performance of HLA-Based SimulationsACM Transactions on Modeling and Computer Simulation (TOMACS)10.1145/271730926:1(1-25)Online publication date: 29-Jun-2015
https://dl.acm.org/doi/10.1145/2717309
Li ZLi XWang LCai WHamilton JRiley GFujimoto R(2014)Hierarchical resource management for enhancing performance of large-scale simulations on data centersProceedings of the 2nd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/2601381.2601390(187-196)Online publication date: 18-May-2014
https://dl.acm.org/doi/10.1145/2601381.2601390
Li ZLi XDuong TCai WTurner SLoper MWainer G(2013)Accelerating optimistic HLA-based simulations in virtual execution environmentsProceedings of the 1st ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/2486092.2486119(211-220)Online publication date: 19-May-2013
https://dl.acm.org/doi/10.1145/2486092.2486119
Toscano LD'Angelo GMarzolla MFilinski AGrelck C(2012)Parallel discrete event simulation with ErlangProceedings of the 1st ACM SIGPLAN workshop on Functional high-performance computing10.1145/2364474.2364487(83-92)Online publication date: 15-Sep-2012
https://dl.acm.org/doi/10.1145/2364474.2364487

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