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The Computational Sprinting Game

Published:25 March 2016Publication History
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Abstract

Computational sprinting is a class of mechanisms that boost performance but dissipate additional power. We describe a sprinting architecture in which many, independent chip multiprocessors share a power supply and sprints are constrained by the chips' thermal limits and the rack's power limits. Moreover, we present the computational sprinting game, a multi-agent perspective on managing sprints. Strategic agents decide whether to sprint based on application phases and system conditions. The game produces an equilibrium that improves task throughput for data analytics workloads by 4-6× over prior greedy heuristics and performs within 90% of an upper bound on throughput from a globally optimized policy.

References

  1. US census data (1990) data set. https://archive.ics.uci.edu/ml/datasets/US+Census+Data (1990).Google ScholarGoogle Scholar
  2. Movielens dataset. http://grouplens.org/datasets/movielens/.Google ScholarGoogle Scholar
  3. Web data commons: Hyperlink graphs. http://webdatacommons.org/hyperlinkgraph/index.html.Google ScholarGoogle Scholar
  4. Dynamic thermal management for high-performance microprocessors. In Proceedings of the 7th IEEE International Symposium on High Performance Computer Architecture (HPCA), pages 171--182. IEEE Computer Society, 2001.Google ScholarGoogle Scholar
  5. Control-theoretic techniques and thermal-RC modeling for accurate and localized dynamic thermal management. In Proceedings of the 8th IEEE International Symposium on High Performance Computer Architecture (HPCA), pages 17--28, 2002.Google ScholarGoogle ScholarCross RefCross Ref
  6. S. Adlakha and R. Johari. Mean field equilibrium in dynamic games with strategic complementarities. Operations Research, 61 (4): 971--989, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  7. S. Adlakha, R. Johari, G. Y. Weintraub, and A. Goldsmith. On oblivious equilibrium in large population stochastic games. In Proceedings of the 49th IEEE Conference on Decision and Control (CDC), pages 3117--3124. IEEE, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  8. S. Adlakha, R. Johari, and G. Y. Weintraub. Equilibria of dynamic games with many players: Existence, approximation, and market structure. Journal of Economic Theory, 2013.Google ScholarGoogle Scholar
  9. Allen-Bradley. Bulletin 1489 UL489 circuit breakers. http://literature.rockwellautomation.com/idc/groups/literature/documents/td/1489-td001_-en-p.pdf.Google ScholarGoogle Scholar
  10. Ametek. Selection and sizing of batteries for UPS backup. http://www.solidstatecontrolsinc.com/download/selection-and-sizing-batteries-tech-paper.pdf.Google ScholarGoogle Scholar
  11. H. Ballani, P. Costa, T. Karagiannis, and A. Rowstron. Towards predictable datacenter networks. In Proceedings of the ACM SIGCOMM Conference (SIGCOMM), pages 242--253. ACM, 2011.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. L. A. Barroso, J. Clidaras, and U. Hölzle. The datacenter as a computer: An introduction to the design of warehouse-scale machines. Synthesis Lectures on Computer Architecture, 8 (3): 1--154, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. A. Beloglazov, J. Abawajy, and R. Buyya. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generation Computer Systems, 28 (5): 755--768, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. J. L. Berral, Í. Goiri, R. Nou, F. Julià, J. Guitart, R. Gavaldà, and J. Torres. Towards energy-aware scheduling in data centers using machine learning. In Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking, pages 215--224. ACM, 2010.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. A. A. Bhattacharya, D. Culler, A. Kansal, S. Govindan, and S. Sankar. The need for speed and stability in data center power capping. Sustainable Computing: Informatics and Systems, 3 (3): 183--193, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  16. J. S. Chase, D. C. Anderson, P. N. Thakar, A. M. Vahdat, and R. P. Doyle. Managing energy and server resources in hosting centers. In Proceedings of the 18th Symposium on Operating Systems Principles (SOSP), pages 103--116. ACM, 2001.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. G. Chen, W. He, J. Liu, S. Nath, L. Rigas, L. Xiao, and F. Zhao. Energy-aware server provisioning and load dispatching for connection-intensive internet services. In Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation (NSDI), pages 337--350. USENIX Association, 2008.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. X. Fan, W.-D. Weber, and L. A. Barroso. Power provisioning for a warehouse-sized computer. In Proceedings of the 34th Annual International Symposium on Computer Architecture (ISCA), pages 13--23. ACM, 2007.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. X. Fu, X. Wang, and C. Lefurgy. How much power oversubscription is safe and allowed in data centers. In Proceedings of the 8th ACM International Conference on Autonomic Computing (ICAC), pages 21--30. ACM, 2011.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. A. Ghodsi, M. Zaharia, B. Hindman, A. Konwinski, S. Shenker, and I. Stoica. Dominant resource fairness: Fair allocation of multiple resource types. In Proceedings of the 8th USENIX Conference on Networked Systems Design and Implementation (NSDI).Google ScholarGoogle Scholar
  21. Í. Goiri, T. D. Nguyen, R. Bianchini, and Í. G. Presa. Coolair: Temperature-and variation-aware management for free-cooled datacenters. In Proceedings of the 20th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), pages 253--265. ACM, 2015.Google ScholarGoogle Scholar
  22. S. Govindan, A. Sivasubramaniam, and B. Urgaonkar. Benefits and limitations of tapping into stored energy for datacenters. In Proceeding of the 38th Annual International Symposium on Computer Architecture (ISCA), pages 341--351. IEEE, 2011.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. S. Govindan, D. Wang, A. Sivasubramaniam, and B. Urgaonkar. Leveraging stored energy for handling power emergencies in aggressively provisioned datacenters. In Proceedings of the 7th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), pages 75--86. ACM, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. M. Guevara, B. Lubin, and B. C. Lee. Navigating heterogeneous processors with market mechanisms. In Proceeding of the 19th IEEE International Symposium on High Performance Computer Architecture (HPCA), pages 95--106. IEEE, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. M. Guevara, B. Lubin, and B. C. Lee. Strategies for anticipating risk in heterogeneous system design. In Proceeding of the 20th IEEE International Symposium on High Performance Computer Architecture (HPCA), pages 154--164. IEEE, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  26. R. Gummadi, R. Johari, and J. Y. Yu. Mean field equilibria of multiarmed bandit games. In Proceedings of the 13th ACM Conference on Electronic Commerce (EC), pages 655--655. ACM, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. B. Hindman, A. Konwinski, M. Zaharia, A. Ghodsi, A. D. Joseph, R. Katz, S. Shenker, and I. Stoica. Mesos: A platform for fine-grained resource sharing in the data center. In Proceedings of the 8th USENIX Conference on Networked Systems Design and Implementation (NSDI), pages 295--308. USENIX Association, 2011.Google ScholarGoogle Scholar
  28. C.-H. Hsu, Y. Zhang, M. Laurenzano, D. Meisner, T. Wenisch, J. Mars, L. Tang, R. G. Dreslinski, et al. Adrenaline: Pinpointing and reining in tail queries with quick voltage boosting. In Proceedings of the 21st IEEE International Symposium on High Performance Computer Architecture (HPCA), pages 271--282. IEEE, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  29. K. Iyer, R. Johari, and M. Sundararajan. Mean field equilibria of dynamic auctions with learning. ACM SIGecom Exchanges, 10 (3): 10--14, 2011.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. M. Le Treust and S. Lasaulce. A repeated game formulation of energy-efficient decentralized power control. Wireless Communications, IEEE Transactions on, 9 (9): 2860--2869, 2010.Google ScholarGoogle Scholar
  31. Y. Li, B. Lee, D. Brooks, Z. Hu, and K. Skadron. CMP design space exploration subject to physical constraints. In Proceedings of the 12th IEEE International Symposium on High Performance Computer Architecture (HPCA), pages 17--28. IEEE, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  32. M. Lin, A. Wierman, L. L. Andrew, and E. Thereska. Dynamic right-sizing for power-proportional data centers. IEEE/ACM Transactions on Networking (TON), 21 (5): 1378--1391, 2013.Google ScholarGoogle Scholar
  33. Z. Liu, A. Wierman, Y. Chen, B. Razon, and N. Chen. Data center demand response: Avoiding the coincident peak via workload shifting and local generation. Performance Evaluation, 70 (10): 770--791, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. D. Lo, L. Cheng, R. Govindaraju, L. A. Barroso, and C. Kozyrakis. Towards energy proportionality for large-scale latency-critical workloads. In Proceeding of the 41st Annual International Symposium on Computer Architecture (ISCA), pages 301--312. IEEE, 2014.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. A. Raghavan. Computational sprinting: Exceeding sustainable power in thermally constrained systems. PhD thesis, University of Pennsylvania, 2013.Google ScholarGoogle Scholar
  36. A. Raghavan, Y. Luo, A. Chandawalla, M. Papaefthymiou, K. P. Pipe, T. F. Wenisch, and M. M. K. Martin. Computational sprinting. In Proceedings of the 18th IEEE International Symposium on High Performance Computer Architecture (HPCA), pages 1--12. IEEE Computer Society, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. A. Raghavan, L. Emurian, L. Shao, M. Papaefthymiou, K. P. Pipe, T. F. Wenisch, and M. M. Martin. Computational sprinting on a hardware/software testbed. In Proceedings of the 18th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), pages 155--166. ACM, 2013.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. A. Raghavan, L. Emurian, L. Shao, M. Papaefthymiou, K. P. Pipe, T. F. Wenisch, and M. M. Martin. Utilizing dark silicon to save energy with computational sprinting. IEEE Micro, 33 (5): 20--28, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  39. A. Raj. CPU hotplug support in Linux™ kernel. URL https://www.kernel.org/doc/Documentation/cpu-hotplug.txt.Google ScholarGoogle Scholar
  40. L. Shao, A. Raghavan, L. Emurian, M. C. Papaefthymiou, T. F. Wenisch, M. M. Martin, and K. P. Pipe. On-chip phase change heat sinks designed for computational sprinting. In Proceedings of the 30th Annual Semiconductor Thermal Measurement and Management Symposium (SEMI-THERM), pages 29--34. IEEE, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  41. M. Skach, M. Arora, C.-H. Hsu, Q. Li, D. Tullsen, L. Tang, and J. Mars. Thermal time shifting: Leveraging phase change materials to reduce cooling costs in warehouse-scale computers. In Proceedings of the 42nd Annual International Symposium on Computer Architecture (ISCA), pages 439--449. IEEE, 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. T. Somu Muthukaruppan, A. Pathania, and T. Mitra. Price theory based power management for heterogeneous multi-cores. In Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS '14, pages 161--176. ACM, 2014.Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. J. Stamper, A. Niculescu-Mizil, S. Ritter, G. Gordon, and K. Koedinger. Algebra I 2006-2007. Challenge data set from KDD Cup 2010 educational data mining challenge. http://pslcdatashop.web.cmu.edu/KDDCup/downloads.jsp.Google ScholarGoogle Scholar
  44. F. Volle, S. V. Garimella, M. Juds, et al. Thermal management of a soft starter: Transient thermal impedance model and performance enhancements using phase change materials. Power Electronics, IEEE Transactions on, 25 (6): 1395--1405, 2010.Google ScholarGoogle Scholar
  45. X. Wang, M. Chen, C. Lefurgy, and T. W. Keller. Ship: A scalable hierarchical power control architecture for large-scale data centers. Parallel and Distributed Systems, IEEE Transactions on, 23 (1): 168--176, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica. Spark: Cluster computing with working sets. In Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing, volume 10, page 10, 2010.Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. S. Zahedi and B. Lee. Sharing incentives and fair division for multiprocessors. IEEE Micro, 35 (3): 92--100, 2015.Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. S. M. Zahedi and B. C. Lee. REF: Resource elasticity fairness with sharing incentives for multiprocessors. In Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), pages 145--160. ACM, 2014.Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. W. Zheng and X. Wang. Data center sprinting: Enabling computational sprinting at the data center level. In Proceedings of the 35th International Conference on Distributed Computing Systems (ICDCS), pages 175--184. IEEE, 2015.Google ScholarGoogle ScholarCross RefCross Ref

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      • Published in

        cover image ACM SIGPLAN Notices
        ACM SIGPLAN Notices  Volume 51, Issue 4
        ASPLOS '16
        April 2016
        774 pages
        ISSN:0362-1340
        EISSN:1558-1160
        DOI:10.1145/2954679
        • Editor:
        • Andy Gill
        Issue’s Table of Contents
        • cover image ACM Conferences
          ASPLOS '16: Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems
          March 2016
          824 pages
          ISBN:9781450340915
          DOI:10.1145/2872362
          • General Chair:
          • Tom Conte,
          • Program Chair:
          • Yuanyuan Zhou

        Copyright © 2016 ACM

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        New York, NY, United States

        Publication History

        • Published: 25 March 2016

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