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Energy-aware job scheduler for high-performance computing

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Computer Science - Research and Development

Abstract

In recent years energy-aware computing has become a major topic, not only in wireless and mobile devices but also in devices using wired technology. The ICT industry is consuming an increasing amount of energy and a large part of the consumption is generated by large-scale data centers. In High-Performance Computing (HPC) data centers, higher performance equals higher energy consumption. This has created incentives on exploring several alternatives to reduce the energy consumption of the system, such as energy-efficient hardware or the Dynamic Voltage and Frequency Scaling (DVFS) technique. This work presents an energy-aware scheduler that can be applied to a HPC data center without any changes in hardware. The scheduler is evaluated with a simulation model and a real-world HPC testbed. Our experiments indicate that the scheduler is able to reduce the energy consumption by 6–16% depending on the job workload. More importantly, there is no significant slowdown in the turnaround time or increase in the wait time of the job. The results hereby evidence that our approach can be beneficial for HPC data center operators without a large penalty on service level agreements.

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References

  1. http://www.hynix.com/products/consumer/consumer_sub.jsp?menuNo=1&m=2&s=1&menu3=01&RK=03&RAM_NAME=DDR2%20SDRAM&SUB_RAM=1Gb

  2. http://www.kingston.com/hyperx/products/khx_ddr2.asp

  3. http://www.samsung.com/global/business/semiconductor/productList.do?fmly_id=696&xFmly_id=695

  4. Bailey Lee C, Schwartzman Y, Hardy J, Snavely A (2005) Are user runtime estimates inherently inaccurate? In: Job scheduling strategies for parallel processing. Lecture notes in computer science, vol 3277, pp 253–263. Springer, Berlin. http://dx.doi.org/10.1007/11407522_14

    Chapter  Google Scholar 

  5. Barroso L, Holzle U (2007) The case for energy-proportional computing. Computer 40(12):33–37. doi:10.1109/MC.2007.443

    Article  Google Scholar 

  6. Basmadjian R, Ali N, Niedermeier F, de Meer H, Giuliani G (2011) A methodology to predict the power consumption of servers in data centers. In: Proceedings of the 2nd international conference on energy-efficient computing and networking 2011 (e-Energy). ACM, New York

    Google Scholar 

  7. Bianzino A, Chaudet C, Larroca F, Rossi D, Rougier J (2010) Energy-aware routing: a reality check. In: GLOBECOM workshops (GC Wkshps). IEEE Press, New York, pp 1422–1427. doi:10.1109/GLOCOMW.2010.5700172

    Google Scholar 

  8. Cirne W, Berman F (2001) A comprehensive model of the supercomputer workload. In: IEEE international workshop on workload characterization (WWC-4), pp 140–148. doi:10.1109/WWC.2001.990753

    Google Scholar 

  9. http://www.raritan.de/px-5000/px-5528/tech-specsde/: Raritan

  10. Etinski M, Corbalan J, Labarta J, Valero M (2010) Utilization driven power-aware parallel job scheduling. Comput Sci Res Dev 25:207–216. http://dx.doi.org/10.1007/s00450-010-0129-x. 10.1007/s00450-010-0129-x

    Article  Google Scholar 

  11. Fan X, Weber WD, Barroso LA (2007) Power provisioning for a warehouse-sized computer. In: Proceedings of the 34th annual international symposium on computer architecture (ISCA’07). ACM, New York, pp 13–23. http://doi.acm.org/10.1145/1250662.1250665

    Chapter  Google Scholar 

  12. http://www.mpiforum.org/docs/: Mpi

  13. Freeh VW, Lowenthal DK, Pan F, Kappiah N, Springer R, Rountree BL, Femal ME (2007) Analyzing the energy-time trade-off in high-performance computing applications. IEEE Trans Parallel Distrib Syst 18:835–848. doi:10.1109/TPDS.2007.1026

    Article  Google Scholar 

  14. Ge R, Feng X, Song S, Chang HC, Li D, Cameron K (2010) Powerpack: energy profiling and analysis of high-performance systems and applications. IEEE Trans Parallel Distrib Syst 21(5):658–671. doi:10.1109/TPDS.2009.76

    Article  Google Scholar 

  15. Girolamo MD, Giuliani G, Egea JCL, Homberg W, Giesler A, Lent R, Mahmoodi T, Sannelli D, Salden A, Georgiadou V, Dang MQ, de Meer H, Basmadjian R, Klingert S, Schulze T (2011) Pilot evaluation of energy control plug-in inside single data centers. Deliverable D-6.2 v8.0, FIT4Green. FP7-ICT-2009-4-249020–FIT4Green/D-6.2, http://www.fit4green.eu/

  16. Hikita J, Hirano A, Nakashima H (2008) Saving 200 KW and $200 k/year by power-aware job/machine scheduling. In: IEEE international symposium on parallel and distributed processing (IPDPS 2008), pp 1–8. doi:10.1109/IPDPS.2008.4536218

    Chapter  Google Scholar 

  17. Hsu Ch, Feng Wc (2005) A power-aware run-time system for high-performance computing. In: Proceedings of the 2005 ACM/IEEE conference on supercomputing (SC’05). IEEE Computer Society, Washington, p 1. http://dx.doi.org/10.1109/SC.2005.3

    Google Scholar 

  18. http://alasir.com/software/ramspeed/: Ramspeed

  19. http://inet.omnetpp.org/: Inet framework

  20. http://omnetpp.org/: Omnet++ network simulation framework

  21. http://www.almico.com/speedfan.php: Speedfan

  22. http://www.coker.com.au/bonnie++/: Bonnie++

  23. http://www.gartner.com/it/page.jsp?id=503867: Gartner newsroom—press release

  24. http://www.netlib.org/linpack/: Linpack

  25. http://www.streambench.org/: Stream

  26. http://www.top500.org/lists/2010/11/highlights: Top500 supercomputer sites

  27. Lent R (2010) Simulating the power consumption of computer networks. In: 15th IEEE international workshop on computer aided modeling, analysis and design of communication links and networks (CAMAD), pp 96–100. doi:10.1109/CAMAD.2010.5686955

    Chapter  Google Scholar 

  28. Liu Y, Zhu H (2010) A survey of the research on power management techniques for high-performance systems. Softw Pract Exp 40:943–964. http://dx.doi.org/10.1002/spe.v40:11

    Article  Google Scholar 

  29. http://www.clusterresources.com/pages/products/torque-resourcemanager.php/: Torque

  30. http://www.openmpi.org/: Openmp

  31. Pinheiro E, Bianchini R, Carrera EV, Heath T (2001) Load balancing and unbalancing for power and performance in cluster-based systems. In: Proceedings of the workshop on compilers and operating systems for low power (COLP)

    Google Scholar 

  32. Ranganathan P, Rivoire S, Moore J (2009) Models and metrics for energy-efficient computing. Adv Comput 75:159–233

    Article  Google Scholar 

  33. Restrepo J, Gruber C, Machuca C (2009) Energy profile aware routing. In: IEEE international conference on communications workshops, pp 1–5. doi:10.1109/ICCW.2009.5208041

    Chapter  Google Scholar 

  34. Rivoire S, Ranganathan P, Kozyrakis C (2008) A comparison of high-level full-system power models. In: Proceedings of the conference on power aware computing and systems (HotPower’08). http://portal.acm.org/citation.cfm?id=1855610.1855613

    Google Scholar 

  35. Rivoire S, Shah M, Ranganatban P, Kozyrakis C, Meza J (2007) Models and metrics to enable energy-efficiency optimizations. Computer 40(12):39–48. doi:10.1007/s00450-011-0189-6

    Article  Google Scholar 

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Correspondence to Olli Mämmelä.

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Mämmelä, O., Majanen, M., Basmadjian, R. et al. Energy-aware job scheduler for high-performance computing. Comput Sci Res Dev 27, 265–275 (2012). https://doi.org/10.1007/s00450-011-0189-6

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  • DOI: https://doi.org/10.1007/s00450-011-0189-6

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