Abstract
Electricity is a significant cost in high performance computing. It can easily exceed the cost of hardware during hardware lifetime. We have studied energy efficiency in a grid computing cluster and noticed that optimising the system configuration can both decrease energy consumption per job and increase throughput. The goal with the proposed saving scheme was that it is easy to implement in normal HPC clusters. Our tests showed that the savings can be up to 25%. The tests were done with real-life high-energy physics jobs.
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An, N., Gurumurthi, S., Sivasubramaniam, A., Vijaykrishnan, N., Kandemir, M., Irwin, M.J.: Energy-performance trade-offs for spatial access methods on memory-resident data. The VLDB Journal 11(3), 179–197 (2002)
Barr, K.C., Asanović, K.: Energy-aware lossless data compression. ACM Trans. Comput. Syst. 24(3), 250–291 (2006)
CERN. LHC Beam-beam Studies, http://lhc-beam-beam.web.cern.ch/lhc-beam-beam
Chen, G., Shetty, R., Kandemir, M., Vijaykrishnan, N., Irwin, M.J., Wolczko, M.: Tuning garbage collection for reducing memory system energy in an embedded java environment. Trans. on Embedded Computing Sys. 1(1), 27–55 (2002)
Conner, S., Link, G.M., Tobita, S., Irwin, M.J., Raghavan, P.: Energy/performance modeling for collective communication in 3-d torus cluster networks. In: SC 2006: Proceedings of the 2006 ACM/IEEE conference on Supercomputing, p. 138. ACM, New York (2006)
Sun Grid Engine. Gridengine - project home (2009), http://gridengine.sunsource.net
Essary, D., Amer, A.: Predictive data grouping: Defining the bounds of energy and latency reduction through predictive data grouping and replication. Trans. Storage 4(1), 1–23 (2008)
CMS Collaboration, Adolphi, R., et al.: The CMS experiment at the CERN LHC. Journal of Instrumentatio 3 (2008)
CMS Experiment. CMSSW Application Framework, https://twiki.cern.ch/twiki/bin/view/CMS/WorkBookCMSSWFramework
Fei, Y., Ravi, S., Raghunathan, A., Jha, N.K.: Energy-optimizing source code transformations for operating system-driven embedded software. Trans. on Embedded Computing Sys. 7(1), 1–26 (2007)
Ge, R., Feng, X., Cameron, K.W.: Performance-constrained distributed dvs scheduling for scientific applications on power-aware clusters. In: SC 2005: Proceedings of the 2005 ACM/IEEE conference on Supercomputing, Washington, DC, USA, p. 34. IEEE Computer Society, Los Alamitos (2005)
Herr, W., Zorzano, M.P.: Coherent dipole modes for multiple interaction regions. Technical report, LHC Project Report 461 (2001)
Jiang, C., Chen, G.: Convergent sparsedt topology control protocol in dense sensor networks. In: InfoScale 2007: Proceedings of the 2nd international conference on Scalable information systems, Brussels, Belgium, pp. 1–8. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) (2007)
Kappiah, N., Freeh, V.W., Lowenthal, D.K.: Just in time dynamic voltage scaling: Exploiting inter-node slack to save energy in mpi programs. In: SC 2005: Proceedings of the 2005 ACM/IEEE conference on Supercomputing, Washington, DC, USA, p. 33. IEEE Computer Society, Los Alamitos (2005)
Li, X., Li, Z., Zhou, Y., Adve, S.: Performance directed energy management for main memory and disks. Trans. Storage 1(3), 346–380 (2005)
Li, Z., Wang, C., Xu, R.: Computation offloading to save energy on handheld devices: a partition scheme. In: CASES 2001: Proceedings of the 2001 international conference on Compilers, architecture, and synthesis for embedded systems, pp. 238–246. ACM, New York (2001)
NorduGrid (2009), http://www.nordugrid.org/middleware
EGEE project (2009), http://www.glite.org
Sadler, C.M., Martonosi, M.: Data compression algorithms for energy-constrained devices in delay tolerant networks. In: SenSys 2006: Proceedings of the 4th International Conference on Embedded Networked Sensor Systems, pp. 265–278. ACM, New York (2006)
Schiele, G., Becker, C., Rothermel, K.: Energy-efficient cluster-based service discovery for ubiquitous computing. In: EW11: Proceedings of the 11th workshop on ACM SIGOPS European workshop, p. 14. ACM, New York (2004)
Torque. Torque resource manager (2009), http://www.clusterresources.com/pages/products/torque-resource-manager.php
Venkatachalam, V., Franz, M.: Power reduction techniques for microprocessor systems. ACM Comput. Surv. 37(3), 195–237 (2005)
Yuan, W., Nahrstedt, K.: Integration of dynamic voltage scaling and soft real-time scheduling for open mobile systems. In: NOSSDAV 2002: Proceedings of the 12th international workshop on Network and operating systems support for digital audio and video, pp. 105–114. ACM, New York (2002)
Zhang, C., Vahid, F., Najjar, W.: A highly configurable cache architecture for embedded systems. SIGARCH Comput. Archit. News 31(2), 136–146 (2003)
Zhang, W., Hu, J.S., Degalahal, V., Kandemir, M., Vijaykrishnan, N., Irwin, M.J.: Reducing instruction cache energy consumption using a compiler-based strategy. ACM Trans. Archit. Code Optim. 1(1), 3–33 (2004)
Zhu, Q., Chen, Z., Tan, L., Zhou, Y., Keeton, K., Wilkes, J.: Hibernator: helping disk arrays sleep through the winter. In: SOSP 2005: Proceedings of the twentieth ACM symposium on Operating systems principles, pp. 177–190. ACM, New York (2005)
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Niemi, T., Kommeri, J., Happonen, K., Klem, J., Hameri, AP. (2009). Improving Energy-Efficiency of Grid Computing Clusters. In: Abdennadher, N., Petcu, D. (eds) Advances in Grid and Pervasive Computing. GPC 2009. Lecture Notes in Computer Science, vol 5529. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01671-4_11
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DOI: https://doi.org/10.1007/978-3-642-01671-4_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01670-7
Online ISBN: 978-3-642-01671-4
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