Skip to main content

Advertisement

Log in

Energy consumption of synchronization algorithms in distributed simulations

  • Published:
Journal of Simulation

Abstract

Power and energy consumption are important concerns in the design of high performance and mobile computing systems, but have not been widely considered in the design of parallel and distributed simulations. The importance of these factors is discussed and metrics for power and energy overhead in parallel and distributed simulations are proposed. Factors affecting the energy consumed by synchronization algorithms and software architectures are examined. An experimental study is presented examining energy consumption of the well-known Chandy/Misra/Bryant and YAWNS synchronization algorithms. The effects of lookahead and event communication on energy use are examined. Initial results concerning queueing network simulations are also presented. The results of this study suggest that existing distributed simulation algorithms require a significant amount of additional energy compared to a sequential execution. Further, different synchronization algorithms can yield different energy consumption behaviors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure  7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12

Similar content being viewed by others

References

  • Bhatti K, Belleudy C and Auguin M (2010). Power management in real time embedded systems through online and adaptive interplay of DPM and DVFS policies. International Conference on Embedded and Ubiquitous Computing. IEEE, Hong Kong pp 184–191.

  • Biswas A and Fujimoto R (2016). Profiling energy consumption in distributed simulations. In: ACM SIGSIM Conference on Principles of Advanced Discrete Simulation (PADS), Banff, ACM.

  • Bryant RE (1977). Simulation of packet communication architecture computer systems M.S. thesis, MIT-LCS-TR-188, Massachusetts Institute of Technology.

  • Chandy KM and Misra J (1979). Distributed simulation: A case study in design and verification of distributed programs. IEEE Transactions on Software Engineering SE-5(5): 440–452.

    Article  Google Scholar 

  • Cho K-M, Liang C-H, Huang J-Y and Yang C-S (2011). Design and implementation of a general purpose power-saving scheduling algorithm for embedded systems. IEEE International Conference on Signal Processing, Communications and Computing. IEEE, Xi’an, pp 1–5.

  • Czechowski K and Vuduc R (2013). A theoretical framework for algorithm-architecture co-design. In: Parallel Distributed Processing (IPDPS), 2013 IEEE 27th International Symposium. pp 791–802.

  • Darema F (2004). Dynamic data driven applications systems: A new paradigm for application simulations and measurements. International Conference on Computational Science. Kraków, Springer: 662–669.

  • Dongarra J, Ltaief H, Luszczek P and Weaver VM (2012). Energy footprint of advanced dense numerical linear algebra using tile algorithms on multicore architecture. In: The 2nd International Conference on Cloud and Green Computing.

  • Esmaeilzadeh H, Cao Yang TX, Blackburn SM and McKinley KS (2012). Looking back and looking forward: power, performance, and upheaval. Commun. ACM 55(7): 105–114.

    Article  Google Scholar 

  • Feng X, Ge R and Cameron KW (2005). Power and energy profiling of scientific applications on distributed systems. In: Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS). p 34.

  • Freeh VW, Lowenthal DK, Pan F, Kappiah N, Springer R, Rountree BL and Femal ME (2007). Analyzing the energy-time trade-off in high-performance computing applications. IEEE Trans. Parallel Distrib. Syst 18(6): 835–848.

    Article  Google Scholar 

  • Ge R, Feng X and Cameron KW (2005). Performance-constrained distributed DVS scheduling for scientific applications on power-aware clusters. In: Proceedings of the 2005 ACM/IEEE conference on Supercomputing. IEEE Computer Society, Washington, DC p 34.

  • Ge R, Feng X, Song S, Chang H-C, Li D and Cameron KW (2010). Powerpack: energy profiling and analysis of high-performance systems and applications. IEEE Transactions on Parallel and Distributed Systems (TPDS) 21(5): 658–671.

    Article  Google Scholar 

  • Gonzalez R, and Horowitz M (1996). Energy dissipation in general purpose microprocessors. IEEE Journal of Solid-State Circuits 31(9): 1277–1284.

    Article  Google Scholar 

  • Grasso I, Radojkovic P, Rajovic N, Gelado I and Ramirez A (2014). Energy Efficient HPC on Embedded SoCs: Optimization Techniques for Mali GPU.In: Parallel and Distributed Processing Symposium, 2014 IEEE 28th International. pp 123–132.

  • Hoeller A, Wanner L and Fröhlich A (2006). A hierarchical approach for power management on mobile embedded systems. In: From Model-Driven Design to Resource Management for Distributed Embedded Systems. pp 265–274.

  • Hua S and Qu G (2003). approaching the maximum energy saving on embedded systems with multiple voltages. IEEE/ACM International Conference on Computer-Aided Design. p 26.

  • Kamrani F and Ayani R (2007). Using on-line simulation for adaptive path planning of UAVs. In: Proceedings of the 11th IEEE International Symposium on Distributed Simulation and Real-Time Applications.

  • Keckler SW, Dally WJ, Khailany B, Garland M and Glasco D (2011). GPUs and the future of parallel computing. Micro, IEEE 31(5): 7–17.

    Article  Google Scholar 

  • Lubachevsky BD (1989). Efficient distributed event-driven simulations of multiple-loop networks. Communications of the ACM 32(1): 111–123.

    Article  Google Scholar 

  • Madey GR, Blake MB, Poellabauer C, Lu H, McCune RR and Wei Y (2012). Applying DDDAS principles to command, control and mission planning for UAV swarms. In: Proceedings of the International Conference on Compuational Science.

  • Neal S, Kanitkar G and Fujimoto RM (2014). Power consumption of data distribution management for on-line simulations. Principles of Advanced Discrete Simulation . Denver, Co., ACM, pp 197–204.

    Google Scholar 

  • Nicol DM (1988). parallel discrete-event simulation of FCFS stochastic queueing networks. SIGPLAN Notices 23(9): 124–137.

    Article  Google Scholar 

  • Nicol DM (1993). The cost of conservative synchronization in parallel discrete event simulations. Journal of the Association for Computing Machinery 40(2): 304-333.

    Article  Google Scholar 

  • Niu L and Quan G (2004). Reducing both dynamic and leakage energy consumption for hard real-time systems. In:International conference on Compilers, architecture, and synthesis for embedded systems pp 140–148.

  • Quan G and Hu X (2001). Energy efficient fixed- priority scheduling for real-time systems on variable voltage processors. Design Automation Conference pp 828–833.

  • Rajovic N, Carpenter PM, Gelado I, Puzovic N, Ramirez A and Valero M (2013). Supercomputing with Commodity CPUs: Are Mobile SoCs Ready for HPC? Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis. New York, NY, USA, ACM: 40: 41–40:12.

  • Rajovic N, Rico A, Vipond J, Gelado I, Puzovic N and Ramirez A (2013). Experiences with mobile processors for energy efficient HPC. Design, Automation Test in Europe Conference Exhibition (DATE), 2013 pp 464–468.

    Google Scholar 

  • Saewong S and Rajkumar R (2003). Practical voltage- scaling for fixed-priority rt-systems. IEEE Real- Time and Embedded Technology and Applications Symposium pp 106–114.

  • Shi W, Perumalla KS and Fujimoto RM (2003). Power-aware state dissemination in mobile distributed virtual environments. In: Parallel and Distributed Simulation, San Diego, pp 181–188.

    Book  Google Scholar 

  • Stanisic L, Videau B, Cronsioe J, Degomme A, Marangozova-Martin V, Legrand A and MehautJ-F (2013). Performance analysis of HPC applications on low-power embedded platforms. Design, Automation Test in Europe Conference Exhibition (DATE), 2013 pp 475–480.

    Google Scholar 

  • Unsal OS (2008). System-level power-aware computing in complex real-time and multimedia systems. Doctor of Philosophy Doctoral Dissertation, University of Massachusetts.

  • Williams S, Waterman A and Patterson D (2009). Roofline: An insightful visual performance model for multicore architectures. Commun. ACM 52(4): 65–76.

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by NSF/AFOSR Grant 1462503.

Authors contribution

The paper discusses importance and relevance of power and energy consumption in the design of high performance and mobile computing systems, which to date has not received a widespread consideration in the design of parallel and distributed simulations. In addition to proposing effective metrics for power and energy overhead in parallel and distributed simulations, we experimentally examine factors affecting the energy consumed by synchronization algorithms and software architectures based on these metrics. The results of these studies suggest that existing distributed simulation algorithms require a significant amount of additional energy compared to a sequential execution. Further, different synchronization algorithms can yield very different energy consumption behaviors for different configurations.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aradhya Biswas.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Biswas, A., Fujimoto, R. Energy consumption of synchronization algorithms in distributed simulations. J Simulation 11, 242–252 (2017). https://doi.org/10.1057/s41273-016-0036-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/s41273-016-0036-7

Keywords

Navigation