ABSTRACT
Energy and power consumption have become important concerns for many computing systems ranging from embedded and mobile systems operating on battery-powered devices to high performance and cloud computing applications running on supercomputers and in data centers. To date, only a limited amount of work has considered power consumption in parallel and distributed simulations. A variety of options to analyze and explore power consumption in distributed simulations are discussed. These options range from design decisions in developing the simulation model to selection of algorithms in distributed simulation middleware to exploitation of hardware techniques. Work to characterize the power and energy consumed by different elements of parallel and distributed simulation systems are discussed and empirical measurements presented to quantify energy and power use, suggestive of directions for future research in this area.
- S. Neal, R. M. Fujimoto, and M. Hunter, "Energy Consumption of Data Driven Traffic Simulations," presented at the Winter Simulation Conference, 2016. Google ScholarDigital Library
- A. Biswas and R. M. Fujimoto, "Energy Consumption of Synchronization Algorithms in Distributed Simulations," Journal of Simulation, 2016.Google Scholar
- Encyclopedia Britanica. (2000, Accessed March 26, 2017). Energy (Physics). Available: https://www.britannica.com/science/energyGoogle Scholar
- Wikipedia. (Accessed March 26, 2017). Battery (Electricity). Available: https://en.wikipedia.org/wiki/Battery_(electricity)Google Scholar
- S. Huck, "Measuring Processor Power," Intel Corporation2011.Google Scholar
- A. Shehabi, S. J. Smith, D. A. Sartor, R. E. Brown, M. Herrlin, J. G. Koomey, et al., "United States Data Center Energy Usage Report," Lawrence Berkeley National Laboratory LBNL-1005775, June 2016.Google Scholar
- L. Benini and G. De Michela, "System-Level Power Optimization: Techniques and Tools," ACM Transactions on Design Automation of Electronic Systems, vol. 5, pp. 115--192, 2000. Google ScholarDigital Library
- IEEE Std 1516.3--2010, IEEE Standard for Modeling and Simulation (M&S) High Level Architecture (HLA) -- Interface Specification. New York, NY: Institute of Electrical and Electronics Engineers, Inc., 2010.Google Scholar
- S. Saewong and R. Rajkumar, "Practical voltage- scaling for fixed-priority rt-systems," presented at the IEEE Real- Time and Embedded Technology and Applications Symposium, 2003. Google ScholarDigital Library
- G. Quan and X. Hu, "Energy efficient fixed- priority scheduling for real-time systems on variable voltage processors," presented at the Design Automation Conference, 2001. Google ScholarDigital Library
- K.-M. Cho, C.-H. Liang, J.-Y. Huang, and C.-S. Yang, "Design and implementation of a general purpose power-saving scheduling algorithm for embedded systems," presented at the IEEE International Conference on Signal Processing, Communications and Computing, Xi'an, China, 2011.Google Scholar
- A. Hoeller, L. Wanner, and A. Fröhlich, "A hierarchical approach for power management on mobile embedded systems," in From Model-Driven Design to Resource Management for Distributed Embedded Systems, ed, 2006, pp. 265--274.Google Scholar
- K. Bhatti, C. Belleudy, and M. Auguin, "Power management in real time embedded systems through online and adaptive interplay of DPM and DVFS policies," presented at the International Conference on Embedded and Ubiquitous Computing, Hong Kong, China, 2010. Google ScholarDigital Library
- L. Niu and G. Quan, "Reducing both dynamic and leakage energy consumption for hard real- time systems," presented at the international conference on Compilers, architecture, and synthesis for embedded systems, 2004. Google ScholarDigital Library
- R. Ge, X. Feng, and K. W. Cameron, "Performance-constrained Distributed DVS Scheduling for Scientific Applications on Power-aware Clusters," presented at the Proceedings of the 2005 ACM/IEEE conference on Supercomputing, Washington, DC, USA, 2005. Google ScholarDigital Library
- V. W. Freeh, D. K. Lowenthal, F. Pan, N. Kappiah, R. Springer, B. L. Rountree, et al., "Analyzing the Energy-Time Trade-Off in High-Performance Computing Applications," IEEE Trans. Parallel Distrib. Syst., vol. 18, pp. 835--848, June 2007. Google ScholarDigital Library
- S. Hua and G. Qu, "Approaching the Maximum Energy Saving on Embedded Systems with Multiple Voltages," presented at the IEEE/ACM International Conference on Computer-Aided Design, 2003. Google ScholarDigital Library
- D. Bedard, A. Porterfield, R. Fowler, and M. Y. Lim, "PowerMon 2: Fine-grained, Integrated Power Measurement,," RENCI, North Carolina, Technical Report TR-09-04, October 2009.Google Scholar
- O. Berry and D. Jefferson, "Critical path analysis of distributed simulation," presented at the Proceedings of the SCS Conference on Distributed Simulation, 1985.Google Scholar
- J. J. Brown, D. Z. Chen, G. W. Greenwood, X. Hu, and R. W. Taylor, "Scheduling for power reduction in a real-time system," presented at the Proceedings of the International Symposium on Low Power Electronics and Design, Monterey, CA, 1997. Google ScholarDigital Library
- D. Kirovski and M. Potkonjak, "System-level synthesis of low-power hard real-time systems," presented at the Proceedings of the Annual Conference on Design Automation, Anaheim, CA, 1997. Google ScholarDigital Library
- B. Dave, G. Lakshminarayana, and N. Jha, "COSYN: Hardware-software co-synthesis for heterogeneous distributed embedded systems," IEEE Transactions on Very Large Scale Integration Systems vol. 7, March 1999. Google ScholarDigital Library
- K. Nagel and M. Schreckenberg, "A Cellular Automata Model for Freeway Traffic," J. Physique I, vol. 2, pp. 2221--2229, 1992.Google ScholarCross Ref
- M. Rickert, K. Nagel, M. Schreckenberg, and A. Latour, "Two lane traffic simulations using cellular automata," Physica A: Statistical Mechanics and its Applications, vol. 231, pp. 534--550, 1996.Google ScholarCross Ref
- D. C. Miller and J. A. Thorpe, "SIMNET: The Advent of Simulator Networking," Proceedings of the IEEE, vol. 83, pp. 1114--1123, 1995.Google ScholarCross Ref
- K.-C. Lin and D. E. Schab, "The Performance Assessment of the Dead Reckoning Algorithms in DIS," Simulation, vol. 63, pp. 318--325, 1994.Google ScholarCross Ref
- R. Saunders, "Formal Expression of Dead Reckoning: Mathematical and Representation Recommendation," presented at the DIS Workshop, 1991.Google Scholar
- W. Shi, K. S. Perumalla, and R. M. Fujimoto, "Power-aware State Dissemination in Mobile Distributed Virtual Environments," in Workshop on Parallel and Distributed Simulation, San Diego, 2003. Google ScholarDigital Library
- R. Fujimoto, Parallel and Distributed Simulation Systems: Wiley Interscience, 2000. Google ScholarDigital Library
- K. M. Chandy and J. Misra, "Distributed Simulation: A Case Study in Design and Verification of Distributed Programs," IEEE Transactions on Software Engineering, vol. SE-5, pp. 440--452, 1979. Google ScholarDigital Library
- R. E. Bryant, "Simulation of Packet Communication Architecture Computer Systems," M.S. thesis, MIT-LCS-TR-188, Computer Science Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, 1977. Google ScholarDigital Library
- D. M. Nicol, "The Cost of Conservative Synchronization in Parallel Discrete Event Simulations," Journal of the Association for Computing Machinery, vol. 40, pp. 304--333, June 1993. Google ScholarDigital Library
- R. M. Fujimoto and A. Biswas, "An Empirical Study of Energy Consumption in Distributed Simulations," presented at the IEEE/ACM International Symposium on Distributed Simulation and Real-Time Applications, 2015. Google ScholarDigital Library
- A. Biswas and R. M. Fujimoto, "Profiling Energy Consumption in Distributed Simulation," presented at the Principles of Advanced Discrete Simulation, 2016. Google ScholarDigital Library
- D. Jefferson, "Virtual Time," ACM Transactions on Programming Languages and Systems, vol. 7, pp. 404--425, 1985. Google ScholarDigital Library
- R. M. Fujimoto, "Performance of Time Warp Under Synthetic Workloads," in Proceedings of the SCS Multiconference on Distributed Simulation. vol. 22, ed, 1990, pp. 23--28.Google Scholar
- A. Boukerche and C. Dzermajko, "Performance Comparison of Data Distribution Management Strategies," in Proceedings of the 5th IEEE International Workshop on Distributed Simulation and Real-Time Applications, Cincinnati, OH, 2001, pp. 67--75. Google ScholarDigital Library
- G. Tan, Y. Zhang, and R. Ayani, "A Hybrid Approach to Data Distribution Management," in Proceedings of the 4th IEEE International Workshop on Distributed Simulation and Real-Time Applications, San Francisco, CA, 2000, pp. 55--61. Google ScholarDigital Library
- A. Boucherche and A. J. Roy, "Dynamic Grid-Based Approach to Data Distribution Management," Journal of Parallel and Distributed Computing, vol. 62, pp. 366--392, 2002. Google ScholarDigital Library
- T. Mudge, "Power: A First-Class Architectural Design Constraint," IEEE Computer, vol. 34, pp. 52--58, 2001. Google ScholarDigital Library
- D. M. Brooks, P. Bose, S. E. Schuster, H. Jacobson, P. N. Kudva, A. Buyuktosunoglu, et al., "Power-aware microarchitecture: Design and modeling challenges for next-generation microprocessors," IEEE Micro, vol. 20, pp. 26--44, 2000. Google ScholarDigital Library
- R. M. Fujimoto, K. S. Perumalla, A. Park, H. Wu, M. Ammar, and G. F. Riley, "Large-Scale Network Simulation -- How Big? How Fast?," in Modeling, Analysis and Simulation of Computer and Telecommunication Systems, 2003.Google Scholar
- R. Ge and K. W. Cameron, "Power-Aware Speedup," presented at the IEEE International Parallel and Distributed Processing Symposium, 2007. IPDPS 2007., 2007.Google Scholar
- V. Soteriou and L.-S. Peh, "Exploring the Design Space of Self-Regulating Power-Aware On/Off Interconnection Networks," presented at the IEEE Transactions Parallel and Distributed Systems. Google ScholarDigital Library
Index Terms
- Power Efficient Distributed Simulation
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