research-article

Green streams for data-intensive software

Authors:

Thomas W. Bartenstein,

Yu David LiuAuthors Info & Claims

ICSE '13: Proceedings of the 2013 International Conference on Software Engineering

Pages 532 - 541

Published: 18 May 2013 Publication History

Abstract

This paper introduces GREEN STREAMS, a novel solution to address a critical but often overlooked property of data-intensive software: energy efficiency. GREEN STREAMS is built around two key insights into data-intensive software. First, energy consumption of data-intensive software is strongly correlated to data volume and data processing, both of which are naturally abstracted in the stream programming paradigm; Second, energy efficiency can be improved if the data processing components of a stream program coordinate in a “balanced” way, much like an assembly line that runs most efficiently when participating workers coordinate their pace. GREEN STREAMS adopts a standard stream programming model, and applies Dynamic Voltage and Frequency Scaling (DVFS) to coordinate the pace of data processing among components, ultimately achieving energy efficiency without degrading performance in a parallel processing environment. At the core of GREEN STREAMS is a novel constraint-based inference to abstract the intrinsic relationships of data flow rates inside a stream program, that uses linear programming to minimize the frequencies – hence the energy consumption – for processing components while still maintaining the maximum output data flow rate. The core algorithm of GREEN STREAMS is formalized, and its optimality is established. The effectiveness of GREEN STREAMS is evaluated on top of the StreamIt framework, and preliminary results show the approach can save CPU energy by an average of 28% with a 7% performance improvement.

References

[1]

J. J. Tran, L. Cinquini, C. A. Mattmann, P. A. Zimdars, D. T. Cuddy, K. S. Leung, O.-I. Kwoun, D. Crichton, and D. Freeborn, “Evaluating cloud computing in the NASA DESDynI ground data system,” in Proceedings of the 2nd International Workshop on Software Engineering for Cloud Computing, ser. SECLOUD ’11, 2011, pp. 36–42.

Digital Library

[2]

C. A. Mattmann, D. J. Crichton, N. Medvidovic, and S. Hughes, “A software architecture-based framework for highly distributed and data intensive scientific applications,” in ICSE ’06, 2006, pp. 721–730.

Digital Library

[3]

R. Mahjourian, “An architectural style for data-driven systems,” in Proceedings of the 10th international conference on Software Reuse: High Confidence Software Reuse in Large Systems, ser. ICSR ’08, 2008, pp. 14–25.

Digital Library

[4]

W. Thies, M. Karczmarek, and S. P. Amarasinghe, “StreamIt: A language for streaming applications,” in Proceedings of the 11th International Conference on Compiler Construction, 2002, pp. 179–196.

Digital Library

[5]

K. Fisher and R. Gruber, “PADS: a domain-specific language for processing ad hoc data,” in PLDI ’05, 2005, pp. 295–304.

Digital Library

[6]

J. Dean and S. Ghemawat, “MapReduce: Simplified data processing on large clusters,” in OSDI’04, 2004.

Digital Library

[7]

Nvidia, “Compute unified device architecture programming guide,” NVIDIA: Santa Clara, CA, 2007.

[8]

G. Wassermann, C. Gould, Z. Su, and P. Devanbu, “Static checking of dynamically generated queries in database applications,” ACM Trans. Softw. Eng. Methodol., vol. 16, no. 4, Sep. 2007.

Digital Library

[9]

W. R. Cook and S. Rai, “Safe query objects: statically typed objects as remotely executable queries,” in ICSE, 2005, pp. 97–106.

Digital Library

[10]

E. S. P. U.S. Environmental Protection Agency, “Report to congress on server and data center energy efficiency public law 109-431,” 2007.

[11]

J. Koomey, “Growth in data center electricity use 2005 to 2010,” August 2011.

[12]

J. Clause, C. Sahin, F. Cayci, I. L. M. Gutierrez, F. Kiamilev, L. Pollock, and K. Winbladh, “Initial explorations on design pattern energy usage,” in Proceedings of Workshop on Green and Sustainable Software (GREENS’12), 2012.

[13]

Y. D. Liu, “Energy-efficient synchronization through program patterns,” in Proceedings of Workshop on Green and Sustainable Software (GREENS’12), 2012.

[14]

W. Baek and T. M. Chilimbi, “Green: a framework for supporting energy-conscious programming using controlled approximation,” in PLDI ’10, 2010, pp. 198–209.

Digital Library

[15]

A. Sampson, W. Dietl, E. Fortuna, D. Gnanapragasam, L. Ceze, and D. Grossman, “EnerJ: Approximate data types for safe and general lowpower computation,” in PLDI’11, Jun. 2011.

Digital Library

[16]

M. Cohen, H. S. Zhu, S. E. Emgin, and Y. D. Liu, “Energy types,” in OOPSLA ’12, October 2012.

Digital Library

[17]

F. Xie, M. Martonosi, and S. Malik, “Compile-time dynamic voltage scaling settings: opportunities and limits,” in PLDI ’03, 2003, pp. 49– 62.

Digital Library

[18]

C.-H. Hsu and U. Kremer, “The design, implementation, and evaluation of a compiler algorithm for CPU energy reduction,” in PLDI ’03, 2003, pp. 38–48.

Digital Library

[19]

M. Weiser, B. Welch, A. Demers, and S. Shenker, “Scheduling for reduced CPU energy,” in OSDI ’94. Berkeley, CA, USA: USENIX Association, 1994, p. 2.

Digital Library

[20]

J. H. Spring, J. Privat, R. Guerraoui, and J. Vitek, “Streamflex: highthroughput stream programming in java,” in OOPSLA ’07, 2007, pp. 211–228.

Digital Library

[21]

J. Zhou and B. Demsky, “Bamboo: a data-centric, object-oriented approach to many-core software,” in PLDI’10. ACM, 2010, pp. 388– 399.

Digital Library

[22]

M. I. Gordon, “Compiler techniques for scalable performance of stream programs on multicore architectures,” Ph.D. dissertation, Massachusetts Institute of Technology, Cambridge, MA, May 2010.

Digital Library

[23]

W. M. Johnston, J. R. P. Hanna, and R. J. Millar, “Advances in dataflow programming languages,” ACM Comput. Surv., vol. 36, pp. 1–34, March 2004.

Digital Library

[24]

T. Reps, S. Horwitz, and M. Sagiv, “Precise interprocedural dataflow analysis via graph reachability,” in POPL ’95, 1995, pp. 49–61.

Digital Library

[25]

P. Haller and M. Odersky, “Scala Actors: Unifying thread-based and event-based programming,” Theor. Comput. Sci., vol. 410, no. 2-3, pp. 202–220, Feb. 2009.

Digital Library

[26]

Google, “The Go language, http://golang.org/.”

[27]

M. Shaw and D. Garlan, Software architecture: perspectives on an emerging discipline. Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 1996.

Digital Library

[28]

L. A. Meyerovich, A. Guha, J. Baskin, G. H. Cooper, M. Greenberg, A. Bromfield, and S. Krishnamurthi, “Flapjax: a programming language for Ajax applications,” in OOPSLA ’09, 2009, pp. 1–20.

Digital Library

[29]

A. Manjhi, S. Nath, and P. B. Gibbons, “Tributaries and deltas: efficient and robust aggregation in sensor network streams,” in SIGMOD ’05, 2005, pp. 287–298.

Digital Library

[30]

S. Madden, M. Shah, J. M. Hellerstein, and V. Raman, “Continuously adaptive continuous queries over streams,” in SIGMOD ’02, 2002, pp. 49–60.

Digital Library

[31]

Z. Wan and P. Hudak, “Functional reactive programming from first principles,” in PLDI ’00, 2000, pp. 242–252.

Digital Library

[32]

T. Pering, T. Burd, and R. Brodersen, “The simulation and evaluation of dynamic voltage scaling algorithms,” in Proceedings of the 1998 international symposium on Low power electronics and design (ISLPED), 1998, pp. 76–81.

Digital Library

[33]

C. Isci, A. Buyuktosunoglu, C.-Y. Cher, P. Bose, and M. Martonosi, “An analysis of efficient multi-core global power management policies: Maximizing performance for a given power budget,” in IEEE/ACM International Symposium on Microarchitecture (MICRO), 2006, pp. 347– 358.

Digital Library

[34]

A. Benoit, P. Renaud-Goud, Y. Robert, and R. Melhem, “Energy-aware mappings of series-parallel workflows onto chip multiprocessors,” in 2011 International Conference on Parallel Processing (ICPP), 2011, pp. 472 –481.

Digital Library

[35]

Y. Yetim, S. Malik, and M. Martonosi, “EPROF: An energy/performance/reliability optimization framework for streaming applications,” in Design Automation Conference (ASP-DAC), 2012 17th Asia and South Pacific, 2012, pp. 769 –774.

[36]

A. Rangasamy and Y. N. Srikant, “Evaluation of dynamic voltage and frequency scaling for stream programs,” in Proceedings of the 8th ACM International Conference on Computing Frontiers (CF), 2011, pp. 40:1– 40:10.

Digital Library

Cited By

Canino ALiu Y(2019)Toward a language design for energy predictionCompanion Proceedings of the 3rd International Conference on the Art, Science, and Engineering of Programming10.1145/3328433.3328445(1-5)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1145/3328433.3328445
Liu YZiarek LBures TWeyns DSchmerl BFitzgerald J(2017)Toward energy-aware programming for unmanned aerial vehiclesProceedings of the 3rd International Workshop on Software Engineering for Smart Cyber-Physical Systems10.5555/3105716.3105724(30-33)Online publication date: 20-May-2017
https://dl.acm.org/doi/10.5555/3105716.3105724
Ribic HLiu Y(2016)AEQUITASProceedings of the 2016 International Conference on Supercomputing10.1145/2925426.2926260(1-12)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1145/2925426.2926260
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Index Terms

Green streams for data-intensive software
1. Software and its engineering
  1. Software creation and management
    1. Designing software
      1. Software implementation planning
        Software design techniques
    2. Software development process management
  2. Software notations and tools

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Information & Contributors

Information

Published In

cover image ACM Conferences

ICSE '13: Proceedings of the 2013 International Conference on Software Engineering

May 2013

1561 pages

ISBN:9781467330763

General Chair:
David Notkin,
Program Chairs:
Betty H. C. Cheng,
Klaus Pohl

Sponsors

SIGSOFT: ACM Special Interest Group on Software Engineering

Publisher

IEEE Press

Publication History

Published: 18 May 2013

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Research-article

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ICSE '13

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SIGSOFT

ICSE '13: 35th International Conference on Software Engineering

May 18 - 26, 2013

CA, San Francisco, USA

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Cited By

Canino ALiu Y(2019)Toward a language design for energy predictionCompanion Proceedings of the 3rd International Conference on the Art, Science, and Engineering of Programming10.1145/3328433.3328445(1-5)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1145/3328433.3328445
Liu YZiarek LBures TWeyns DSchmerl BFitzgerald J(2017)Toward energy-aware programming for unmanned aerial vehiclesProceedings of the 3rd International Workshop on Software Engineering for Smart Cyber-Physical Systems10.5555/3105716.3105724(30-33)Online publication date: 20-May-2017
https://dl.acm.org/doi/10.5555/3105716.3105724
Ribic HLiu Y(2016)AEQUITASProceedings of the 2016 International Conference on Supercomputing10.1145/2925426.2926260(1-12)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1145/2925426.2926260
Moura IPinto GEbert FCastor FPenta MPinzger MRobbes R(2015)Mining energy-aware commitsProceedings of the 12th Working Conference on Mining Software Repositories10.5555/2820518.2820528(56-67)Online publication date: 16-May-2015
https://dl.acm.org/doi/10.5555/2820518.2820528
Pinto GSoares-Neto FCastor FLago PMüller H(2015)Refactoring for energy efficiencyProceedings of the Fourth International Workshop on Green and Sustainable Software10.5555/2820158.2820165(29-35)Online publication date: 16-May-2015
https://dl.acm.org/doi/10.5555/2820158.2820165
Zhu HLin CLiu YBertolino ACanfora GElbaum S(2015)A programming model for sustainable softwareProceedings of the 37th International Conference on Software Engineering - Volume 110.5555/2818754.2818847(767-777)Online publication date: 16-May-2015
https://dl.acm.org/doi/10.5555/2818754.2818847
Lin CKuan CShih WLee J(2015)Compilers for Low Power with Design Patterns on Embedded Multicore SystemsJournal of Signal Processing Systems10.1007/s11265-014-0917-980:3(277-293)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1007/s11265-014-0917-9
Pinto GCastor FLiu Y(2014)Understanding energy behaviors of thread management constructsACM SIGPLAN Notices10.1145/2714064.266023549:10(345-360)Online publication date: 15-Oct-2014
https://dl.acm.org/doi/10.1145/2714064.2660235
Bartenstein TLiu Y(2014)Rate types for stream programsACM SIGPLAN Notices10.1145/2714064.266022549:10(213-232)Online publication date: 15-Oct-2014
https://dl.acm.org/doi/10.1145/2714064.2660225
Pinto GCastor FLiu YBlack AMillstein T(2014)Understanding energy behaviors of thread management constructsProceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages & Applications10.1145/2660193.2660235(345-360)Online publication date: 15-Oct-2014
https://dl.acm.org/doi/10.1145/2660193.2660235
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