Skip to main content
SpringerLink
Log in

Energy Consumption Analysis of HPC Applications Using NoSQL Database Feature of EnergyAnalyzer

  • Conference paper
  • First Online:
Book cover Intelligent Cloud Computing (ICC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8993))

Included in the following conference series:

  • 810 Accesses

Abstract

A notion of increasing the energy efficiency of HPC machines or applications has reached the global HPC community forum in recent years. This has opened up several interesting possibilities that reduces the energy consumption of applications, including an energy consumption analysis mechanism which delves into the reason behind the energy consumption bottlenecks of applications. In order to easily analyze the energy consumption of applications (from machine to machine), a need for a dedicated energy consumption analysis tool has undoubtedly enthused application developers or users. In general, when applications were analyzed for performance bottlenecks in modern HPC architectures, such as, exascale machines which have more than tens of thousands of cores, a performance analysis tool might deliver a huge performance dataset. Querying such data in a short span of time can efficiently be done using document based NoSQL database systems. This paper proposes an online-based energy consumption analysis mechanism of HPC applications using EnergyAnalyzer Performance Database (EAPerfDB), a NoSQL-based performance database feature, of EnergyAnalyzer tool. The EnergyAnalyzer tool uses semantic agents in a distributed fashion to undergo the energy consumption analysis of HPC applications. In addition, the paper explores the findings of the energy consumption analysis of High Performance Computing Challenge (HPCC) benchmarks when NoSQL-based EnergyAnalyzer tool was used at the HPCCLoud Research Laboratory of our premise.

This work is partially funded by the Department of Science and Technology of India under FAST Young Scientist Scheme - Engineering Sciences division (Grant No: SR/FTP/ETA-93/2011) and it is motivated by CIM-GIZ, Germany. For more details, visit www.sxcce.edu.in/hpccloud.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 34.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 44.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Amazon HPC, Amazon HPC services (2014). https://aws.amazon.com/hpc/

  2. Anand Sivasubramaniam Make IT Green: The TCS way, Tech report, pp. 1–12 (2008). www.tcs.com/tcs_innovation_whitepaper_Make-IT-Green.pdf

  3. Cheng, Y., Zeng, Y.: Automatic energy status controlling with dynamic voltage scaling in power-aware high performance computing cluster. In: Proceedings of 12th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), pp. 412–416 (2011)

    Google Scholar 

  4. Whaley, R.C., Dongarra, J.J.: Automatically tuned linear algebra software. In: Proceedings of the 1998 ACM/IEEE Conference on Supercomputing (SC 1998). IEEE Computer Society, Washington, D.C., pp. 1–27 (1998)

    Google Scholar 

  5. Compiler, Compiler Optimization Switches (2014). http://gcc.gnu.org/onlinedocs/gcc/Optimize-Options.html

  6. Conejero, J., Rana, O., Burnap, P., Morgan, J., Carrin, C., Caminero, B.: Characterising the power consumption of hadoop clouds - a social media analysis case study. In: Proceedings of CLOSER, pp. 233–243 (2013)

    Google Scholar 

  7. Cooper, K.D., Schielke, P.J., Subramanian, D.: Optimizing for reduced code space using genetic algorithms. In: Proceedings of the Conference on Languages, Compilers, and Tools for Embedded Systems (LCTES), p. 19 (1999)

    Google Scholar 

  8. Dagstuhl participants (2013). http://www.dagstuhl.de/program/calendar/partlist/?semnr=13401&SUOG

  9. Google Compute Engine and NoSQL. http://www.infoq.com/news/2013/05/google-compute-engine

  10. GreenLists, Top 500 List of Green Supercomputers (2014). http://www.green500.org/news/green500-list-november-2013

  11. Herbert, J., Peter, T., Durillo, J.J., Simone, P., Philipp, G., Thomas, F., Moritsch, H.: A multi-objective auto-tuning framework for parallel codes. In: SC 2012 (2012)

    Google Scholar 

  12. hpcc. http://icl.cs.utk.edu/hpcc/

  13. Adhianto, L., Banerjee, S., Fagan, M., Krentel, M., Marin, G., Crummey, J.M., Tallent, N.R.: HPCToolkit: tools for performance analysis of optimized parallel programs. Concurrency Comput. Pract. Exp. 22(2), 685–701 (2010)

    Google Scholar 

  14. Intel In-built Sensors, Running Average Power Limit for Xeon Processors, July 2012. http://www.intel.com/xeon

  15. Layton, J.: The Cloud’s Role in HPC (2014). http://www.admin-magazine.com/HPC/Articles/The-Cloud-s-Role-in-HPC

  16. Knobloch, M., Mohr, B., Minartz, T.: Determine energy-saving potential in wait-states of large-scale parallel programs. Comput. Sci. Res. Dev. 27(4), 255–263 (2011)

    Article  Google Scholar 

  17. Le, K., Ricardo, B., Zhang, J., Yogesh, J., Meng, J., Nguyen, T.D.: Reducing electricity cost through virtual machine placement in high performance computing clouds. In: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2011, pp. 1–22 (2011). doi:10.1145/2063384.2063413

  18. LRZ Supercomputer Information. http://www.lrz.de/services/compute/supermuc/systemdescription/

  19. LucidWorks, LucidWorks Integrates with MongoDB (2013). http://archive.hpcwire.com/hpccloud/2013-03-18/lucidworks_integrates_with_mongodb.html

  20. Malony, A., Biersdorff, S., Shende, S., Jagode, H., Tomov, S., Juckeland, G., Dietrich, R., Poole, D., Lamb, C.: Parallel performance measurement of heterogeneous parallel systems with GPUs. In: International Conference on Parallel Processing ICPP 2011, Taipei, Taiwan, pp. 13–16 (2011)

    Google Scholar 

  21. Hahnel, M., Dobel, B., Volp, M., Hartig, H.: Measuring Energy Consumption for Short Code Paths Using RAPL, July 2012. www.sigmetrics.org/greenmetrics/Hahnel.pdf

  22. MongoDB. http://docs.mongodb.org/manual/faq/concurrency/

  23. MontBlanc Project (2014). http://www.montblanc-project.eu

  24. Niels, B.: New Intel Xeon Phi Coprocessors to have Xeon CPUs On Board (2013). http://insidehpc.com/2013/05/24/new-intel-xeon-phi-coprocessors-to-have-xeons-on-board/

  25. NoSQL. http://nosql-database.org/

  26. NoSQL is useful. http://www.infoq.com/news/2013/04/gartner-technology-trends/

  27. Geimer, M., Saviankou, P., Strube, A., Szebenyi, Z., Wolf, F., Wylie, B.J.N.: Further improving the scalability of the scalasca toolset. In: Jónasson, K. (ed.) PARA 2010, Part II. LNCS, vol. 7134, pp. 463–473. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  28. Wright, N.J., Pfeiffer, W., Snavely, A.: Characterizing parallel scaling of scientific applications using IPM. In: The 10th LCI International Conference on High-Performance Clustered Computing, Boulder, pp. 1–21 (2009)

    Google Scholar 

  29. Penguin Computing, Penguin Computing on Demand (2014). http://www.penguincomputing.com/services/hpc-cloud/pod

  30. PowerAdvisor, HP Power Advisor utility: a tool for estimating power requirements for HP ProLiant server systems, July 2012. http://h20000.www2.hp.com/bc/docs/support/SupportManual/c01861599/c01861599.pdf

  31. RCloud, Rcloud from R-HPC (2014). http://www.r-hpc.com/

  32. Sachs, K., Kounev, S., Buchmann, A.: Performance modeling and analysis of message-oriented event-driven systems. Softw. Syst. Model. 12(4), 705–729 (2012). doi:10.1007/s10270-012-0228-1

    Article  Google Scholar 

  33. Benedict, S., Gerndt, M.: Automatic performance analysis of OpenMP codes on a scalable shared memory system using periscope. In: Jónasson, K. (ed.) PARA 2010, Part II. LNCS, vol. 7134, pp. 452–462. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  34. Benedict, S., Rejitha, R.S., Bright, C.B.: Energy consumption-based performance tuning of software and applications using particle swarm optimization. In: 6th IEEE CSI International Conference on Software Engineering (CONSEG) 2012, pp. 1–6 (2012)

    Google Scholar 

  35. Benedict, S.: Performance issues and performance analysis tools for HPC cloud applications: a survey. Computing 95(2), 89–108 (2013). doi:10.1007/s00607-012-0213-0

    Article  MATH  Google Scholar 

  36. Benedict, S.: Energy-aware performance analysis methodologies for HPC architectures - an exploratory study. J. Netw. Comput. Appl. 35(6), 1709–1719 (2012)

    Article  Google Scholar 

  37. Song, S., Grove, M., Cameron, K.W.: An iso-energy-efficient approach to scalable system power-performance optimization. In: Proceedings of the IEEE International Conference on Cluster Computing (Cluster 2011), Austin, Texas, pp. 262–271, September 2011

    Google Scholar 

  38. Shende, S., Malony, A.D.: The TAU parallel performance system. Int. J. High Perform. Comput. 20(2), 287–311 (2006)

    Article  Google Scholar 

  39. Do, T., Rowshdeh, S., Shi, W.: pTop: A Process-level Power Profiling Tool, July 2012. www.sigops.org/sosp/sosp09/papers/hotpower_13_do.pdf

  40. Viswanathan, H., Lee, E.K., Rodero I., Pompili D., Parashar M., Gamell M.: Energy-aware application-centric VM allocation for HPC workloads. In: 2011 IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), pp. 890–897 (2011). doi:10.1109/IPDPS.2011.234

  41. Simmhan, Y., Noor, M.U.: Scalable prediction of energy consumption using incremental time series clustering. In: BigData Conference, pp. 29–36 (2013)

    Google Scholar 

Download references

Acknowledgment

Shajulin Benedict thanks Prof. Michael Gerndt (TUM, Germany) for providing a research motivation. He thanks DST, India and Ms. Pragya Taneja (CIM-GIZ, Germany) for providing the financial and moral support to carry out this research work. In addition, he thanks the reviewers and organizers of ICC2014.

Author information

Authors and Affiliations

  1. SXCCE, Nagercoil, India

    Shajulin Benedict, R. S. Rejitha & C. Bright

Authors
  1. Shajulin Benedict
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. S. Rejitha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. C. Bright
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shajulin Benedict .

Editor information

Editors and Affiliations

  1. Sultan Qaboos University, Muscat, Oman

    Asma Al-Saidi

  2. GUtech, Muscat, Oman

    Rudolf Fleischer

  3. Zayed University, Dubai, Utd.Arab.Emir.

    Zakaria Maamar

  4. Cardiff University, Cardiff, United Kingdom

    Omer F. Rana

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Benedict, S., Rejitha, R.S., Bright, C. (2015). Energy Consumption Analysis of HPC Applications Using NoSQL Database Feature of EnergyAnalyzer. In: Al-Saidi, A., Fleischer, R., Maamar, Z., Rana, O. (eds) Intelligent Cloud Computing. ICC 2014. Lecture Notes in Computer Science(), vol 8993. Springer, Cham. https://doi.org/10.1007/978-3-319-19848-4_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-19848-4_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19847-7

  • Online ISBN: 978-3-319-19848-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation