article

Energy Measurement Tools for Ultrascale Computing: A Survey

Authors:

Francisco Almeida,

Javier Arteaga,

Vicente Blanco,

Alberto CabreraAuthors Info & Claims

Supercomputing Frontiers and Innovations: an International Journal, Volume 2, Issue 2

Pages 64 - 76

https://doi.org/10.14529/jsfi150204

Published: 06 April 2015 Publication History

Abstract

With energy efficiency one of the main challenges on the way towards ultrascale systems, there is a great need for access to high-quality energy consumption data. Such data would enable researchers and designers to pinpoint energy inefficiencies at all levels of the computing stack, from whole nodes down to critical regions of code. However, measurement capabilities are often missing, and significantly differ between platforms where they exist. A standard is yet to be established. To that end, this paper attempts an extensive survey of energy measurement tools currently available at both the hardware and software level, comparing their features with respect to energy monitoring.

References

[1]

Advanced Micro Devices. AMD BIOS and Kernel Developer's Guide BKDG for AMD Family 15h Models 00h-0Fh Processors, January 2013.

[2]

Pedro Alonso, Rosa M Badia, Jesus Labarta, Maria Barreda, Manuel F Dolz, Rafael Mayo, Enrique S Quintana-Orti, and Ruyman Reyes. Tools for power-energy modelling and analysis of parallel scientific applications. In Parallel Processing ICPP, 2012 41st International Conference on, pages 420-429. IEEE, 2012.

Digital Library

[3]

ARM Limited. ARM Energy Probe. http://ds.arm.com/ds-5/optimize/arm-energy-probe/.

[4]

D. Bedard, Min Yeol Lim, R. Fowler, and A. Porterfield. Powermon: Fine-grained and integrated power monitoring for commodity computer systems. In IEEE South eastCon 2010 SoutheastCon, Proceedings of the, pages 479-484, March 2010.

[5]

Daniel Bedard, R Fowler, M Linn, and Allan Porterfield. Powermon 2: Fine-grained, integrated power measurement. Renaissance Computing Institute, Tech. Rep. TR-09-04, 2009.

[6]

Shajulin Benedict. Energy-aware performance analysis methodologies for HPC architectures-an exploratory study. Journal of Network and Computer Applications, 356: 1709-1719, 2012.

Digital Library

[7]

Shajulin Benedict, Ventsislav Petkov, and Michael Gerndt. Periscope: An online-based distributed performance analysis tool. In Tools for High Performance Computing 2009, pages 1-16. Springer, 2010, 1.

[8]

S. Browne, J. Dongarra, N. Garner, G. Ho, and P. Mucci. A portable programming interface for performance evaluation on modern processors. Int. J. High Perform. Comput. Appl., 143:189-204, August 2000.

Digital Library

[9]

Martin Burtscher, Ivan Zecena, and Ziliang Zong. Measuring GPU power with the K20 builtin sensor. In Proceedings of Workshop on General Purpose Processing Using GPUs, GPGPU-7, pages 28:28-28:36, New York, NY, USA, 2014. ACM.

[10]

APC by Schneider Elecric. Metered-by-outlet rack pdus, March 2013.

[11]

Alberto Cabrera, Francisco Almeida, Javier Arteaga, and Vicente Blanco. Measuring energy consumption using EML Energy Measurement Library. Computer Science-Research and Development, pages 1-9, 2014.

Digital Library

[12]

Alberto Cabrera, Francisco Almeida, and Vicente Blanco. Eml, an energy measurement library. 31st International Symposium on Computer Performance, Modeling, Measurements and Evaluation 2013: Student Poster Abstracts, 2013.

[13]

Jason Flinn and Mahadev Satyanarayanan. Powerscope: A tool for profiling the energy usage of mobile applications. In WMCSA, pages 2-10. IEEE Computer Society, 1999.

Digital Library

[14]

Rong Ge, Xizhou Feng, Shuaiwen Song, Hung-Ching Chang, Dong Li, and K.W. Cameron. Powerpack: Energy profiling and analysis of high-performance systems and applications. Parallel and Distributed Systems, IEEE Transactions on, 215:658-671, May 2010.

Digital Library

[15]

Markus Geimer, Felix Wolf, Brian JN Wylie, Erika Abraham, Daniel Becker, and Bernd Mohr. The scalasca performance toolset architecture. Concurrency and Computation: Practice and Experience, 226:702-719, 2010.

Digital Library

[16]

D. Hackenberg, T. Ilsche, R. Schone, D. Molka, M. Schmidt, and W.E. Nagel. Power measurement techniques on standard compute nodes: A quantitative comparison. In Performance Analysis of Systems and Software ISPASS, 2013 IEEE International Symposium on, pages 194-204, April 2013.

[17]

Daniel Hackenberg, Thomas Ilsche, Joseph Schuchart, Robert chone, Wolfgang E. Nagel, Marc Simon, and iannis Georgiou. Hdeem: High definition energy efficiency monitoring. In Proceedings of the 2Nd International Workshop on Energy Efficient upercomputing, E2SC '14, pages 1-10, Piscataway, NJ, USA, 2014. IEEE Press.

Digital Library

[18]

Nicole Hemsoth. Are Supercomputing's Elite Turning Backs on Accelerators? http://www.hpcwire.com/2014/06/26/accelerators-hold/, June 2014.

[19]

Chung-Hsing Hsu and S.W. Poole. Power measurement for high performance computing: State of the art. In Green Computing Conference and Workshops IGCC, 2011 International, pages 1-6, July 2011.

Digital Library

[20]

Intel Corporation. Data Center Manageability Interface Specification, August 2011.

[21]

Intel Corporation. Intelligent Platform Management Interface Spec, October 2013.

[22]

Intel Corporation. Determining the Idle Power of an Intel R Xeon PhiTM Coprocessor. https://software.intel.com/en-us/articles/determining-the-idle-power-of-an-intel-xeon-phi-coprocessor, June 2014.

[23]

Intel Corporation. Intel R Xeon PhiTM Coprocessor Datasheet. http://www.intel.com/content/www/us/en/processors/xeon/xeon-phi-coprocessor-datasheet.html, April 2014.

[24]

Intel Corporation. Intel R 64 and IA-32 Architectures Software Developer's Manual. Number 253669-053US. January 2015.

[25]

Kiran Kasichayanula, Dan Terpstra, Piotr Luszczek, Stan Tomov, Shirley Moore, and Gregory D Peterson. Power aware computing on gpus. In Application Accelerators in High Performance Computing SAAHPC, 2012 Symposium on, pages 64-73. IEEE, 2012.

Digital Library

[26]

kernel.org. Perf Wiki. Rlhttps://perf.wiki.kernel.org/index.php?title=Main Page&oldid=3491, 2014.

[27]

Andreas Knupfer, Christian Rossel, Dieter an Mey, Scott Biersdorff, Kai Diethelm, Dominic Eschweiler, Markus Geimer, Michael Gerndt, Daniel Lorenz, Allen Malony, et al. Score-p: A joint performance measurement run-time infrastructure for periscope, scalasca, tau, and vampir. In Tools for High Performance Computing 2011, pages 79-91. Springer, 2012.

[28]

James H Laros, Phil Pokorny, and David DeBonis. Powerinsight-a commodity power measurement capability. In Green Computing Conference IGCC, 2013 International, pages 1-6. IEEE, 2013.

[29]

Heike McCraw, James Ralph, Anthony Danalis, and Jack Dongarra. Power monitoring with papi for extreme scale architectures and dataflow-based programming models. 2014.

[30]

John Mellor-Crummey, Robert J Fowler, Gabriel Marin, and Nathan Tallent. Hpcview: A tool for top-down analysis of node performance. The Journal of Supercomputing, 231:81-104, 2002.

Digital Library

[31]

Wolfgang E Nagel, Alfred Arnold, MichaelWeber, Hans-Christian Hoppe, and Karl Solchenbach. Vampir: Visualization and analysis of mpi resources. 1996.

[32]

National Instruments Corporation. What Is Data Acquisition? http://www.ni.com/data-acquisition/what-is/.

[33]

Adel Noureddine, Romain Rouvoy, and Lionel Seinturier. A review of energy measurement approaches. ACM SIGOPS Operating Systems Review, 473:42-49, 2013.

Digital Library

[34]

NVIDIA Corporation. NVML API Reference Guide, March 2014.

[35]

P3 International. Kill A Watt product page. http://www.p3international.com/products/p4400.html.

[36]

Vincent Pillet, Jesus Labarta, Toni Cortes, and Sergi Girona. Paraver: A tool to visualize and analyze parallel code. In Proceedings of WoTUG-18: Transputer and occam Developments, volume 44, pages 17-31. mar, 1995.

[37]

Sebastian Ryffel. Lea2p: The linux energy attribution and accounting platform. Master's thesis, Swiss Federal Institute of Technology, 2009.

[38]

Sandia National Laboratories. High performance computing power application programming interface api specification. http://powerapi.sandia.gov/, 2014.

[39]

Schleifenbauer. Schleifenbauer operation manual. http://schleifenbauer.eu/dynamisch/bibliotheek/16_0_NL_usermanual_v2.1.pdf.

[40]

Alain Schuermans. Schleifenbauer products bv, March 2012.

[41]

Martin Schulz, Jim Galarowicz, Don Maghrak, William Hachfeld, David Montoya, and Scott Cranford. Open speedshop: An open source infrastructure for parallel performance analysis. Scientific Programming, 162-3:105-121, 2008.

Digital Library

[42]

Sameer S Shende and Allen D Malony. The tau parallel performance system. International Journal of High Performance Computing Applications, 202:287-311, 2006.

Digital Library

[43]

Thanos Stathopoulos, Dustin McIntire, and William J. Kaiser. The energy endoscope: Realtime detailed energy accounting for wireless sensor nodes. In IPSN, pages 383-394. IEEE Computer Society, 2008.

Digital Library

[44]

Lus Tanica, Aleksandar Ilic, Pedro Tomas, and Leonel Sousa. Schedmon: A performance and energy monitoring tool for modern multi-cores. In Euro-Par 2014: Parallel Processing Workshops, pages 230-241. Springer, 2014, 20.

[45]

Valerie E Taylor, Xingfu Wu, Jonathan Geisler, Xin Li, Zhiling n, Rick Stevens, Mark Hereld, and Ivan R Judson. Prophesy: An infrastructure for analyzing and modeling the performance parallel and distributed applications. In High-Performance Distributed Computing, 2000. Proceedings. The nth International Symposium on, pages 302-303. IEEE, 2000.

Digital Library

[46]

J. Treibig, G. Hager, and G. Wellein. Likwid: A lightweight performance-oriented tool suite for x86 multicore environments. In Proceedings of PSTI2010, the First International Workshop on Parallel Software Tools and Tool Infrastructures, San Diego CA, 2010.

Digital Library

[47]

Unified EFI, Inc. Advanced Configuration and Power Interface Specification, July 2014.

[48]

Virtual Institute High Productivity Supercomputing. Score-E. http://www.vi-hps.org/projects/score-e/, 2013.

[49]

Watt's Up Meters. Operators Manual. https://www.wattsupmeters.com/secure/downloads/manual_rev_9_corded0812.pdf.

[50]

Watt's Up Meters. Watt's Up product page. https://www.wattsupmeters.com/.

[51]

V.Weaver, M. Johnson, K. Kasichayanula, J. Ralph, P. Luszczek, D. Terpstra, and S. Moore. Measuring energy and power with papi. In International Workshop on Power-Aware Systems and Architectures, Pittsburgh, PA, September 2012.

Digital Library

[52]

Vincent M Weaver, Daniel Terpstra, Heike McCraw, Matt Johnson, Kiran Kasichayanula, James Ralph, John Nelson, Philip Mucci, Tushar Mohan, and Shirley Moore. Papi 5: Measuring power, energy, and the cloud. In Performance Analysis of Systems and Software ISPASS, 2013 IEEE International Symposium on, pages 124-125. IEEE, 2013.

[53]

Xingfu Wu, Charles Lively, Valerie Taylor, Hung-Ching Chang, Chun-Yi Su, Kirk Cameron, Shirley Moore, Daniel Terpstra, and Vince Weaver. Mummi: multiple metrics modeling infrastructure. In Software Engineering, Artificial Intelligence, Networking and Parallel/ Distributed Computing SNPD, 2013 14th ACIS International Conference on, pages 289-295. IEEE, 2013.

Digital Library

[54]

Yokogawa Electric Corporation. Power Analyzers. http://tmi.yokogawa.com/products/digital-power-analyzers/.

[55]

K. Yoshii, K. Iskra, R. Gupta, P. Beckman, V. Vishwanath, Chenjie Yu, and S. Coghlan. Evaluating power-monitoring capabilities on ibm blue gene/p and blue gene/q. In Cluster Computing CLUSTER, 2012 IEEE International Conference on, pages 36-44, September 2012.

Digital Library

[56]

ZES ZIMMER. Brochure LMG450. http://www.zes.com/en/content/download/286/2473/file/lmg450_prospekt_1002_e.pdf.

[57]

ZES ZIMMER Electronic Systems GmbH. Products: Precision Power Analyzer. http://www.zes.com/en/Products/Precision-Power-Analyzer.

Cited By

Cabrera ANichita PAfonso SAlmeida FBlanco V(2022)Reliable Energy Measurement on Heterogeneous Systems–on–Chip Based EnvironmentsParallel Processing and Applied Mathematics10.1007/978-3-031-30442-2_28(371-382)Online publication date: 11-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-30442-2_28
Cabrera AAcosta AAlmeida FBlanco V(2019)A heuristic technique to improve energy efficiency with dynamic load balancingThe Journal of Supercomputing10.1007/s11227-018-2718-675:3(1610-1624)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s11227-018-2718-6

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cover image Supercomputing Frontiers and Innovations: an International Journal

Supercomputing Frontiers and Innovations: an International Journal Volume 2, Issue 2

April 2015

126 pages

ISSN:2409-6008

EISSN:2313-8734

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South Ural State University

Chelyabinsk, Russian Federation

Publication History

Published: 06 April 2015

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Cited By

Cabrera ANichita PAfonso SAlmeida FBlanco V(2022)Reliable Energy Measurement on Heterogeneous Systems–on–Chip Based EnvironmentsParallel Processing and Applied Mathematics10.1007/978-3-031-30442-2_28(371-382)Online publication date: 11-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-30442-2_28
Cabrera AAcosta AAlmeida FBlanco V(2019)A heuristic technique to improve energy efficiency with dynamic load balancingThe Journal of Supercomputing10.1007/s11227-018-2718-675:3(1610-1624)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1007/s11227-018-2718-6

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