publication . Article . Preprint . 2016

Tools for assessing and optimizing the energy requirements of high performance scientific computing software

Kai Diethelm;
Open Access English
  • Published: 04 May 2016 Journal: PAMM, volume 16, issue 16,177,061, pages 837-838 (issn: 16177061, Copyright policy)
Abstract
Score-P is a measurement infrastructure originally designed for the analysis and optimization of the performance of HPC codes. Recent extensions of Score-P and its associated tools now also allow the investigation of energy-related properties and support the user in the implementation of corresponding improvements. Since it would be counterproductive to completely ignore performance issues in this connection, the focus should not be laid exclusively on energy. We therefore aim to optimize software with respect to an objective function that takes into account energy and run time.
doi: 10.1002/pamm.201610407
Subjects
free text keywords: D.2.8, C.1.4, C.4, 68M20, 68N30, 68W40, Computer Science - Software Engineering, Software, business.industry, business, Systems engineering, Energy requirement, Computer science
Funded by
EC| READEX
Project
READEX
Runtime Exploitation of Application Dynamism for Energy-efficient eXascale computing
  • Funder: European Commission (EC)
  • Project Code: 671657
  • Funding stream: H2020 | RIA
Communities
FET H2020FET HPC: HPC Core Technologies, Programming Environments and Algorithms for Extreme Parallelism and Extreme Data Applications
FET H2020FET HPC: Runtime Exploitation of Application Dynamism for Energy-efficient eXascale computing
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[1] C. Bischof, D. an Mey, and C. Iwainsky, Brainware for green HPC, Computer Science | Research and Development 27, 227{233 (2012).

[2] Leibniz-Rechenzentrum der Bayerischen Akademie der Wissenschaften, Working with energy aware jobs on SuperMUC, https: //www.lrz.de/services/compute/supermuc/loadleveler/#energy.

[3] I. Zhukov, C. Feld, M. Geimer, M. Knobloch, B. Mohr, and P. Saviankou, Scalasca v2: Back to the future, in: Tools for High Performance Computing 2014, edited by C. Niethammer, J. Gracia, A. Knupfer, M. M. Resch, and W. E. Nagel (Springer, Cham, 2015), pp. 1{24.

[4] H. Brunst, D. Hackenberg, G. Juckeland, and H. Rohling, Comprehensive performance tracking with Vampir 7, in: Tools for High Performance Computing, edited by M. S. Muller, M. M. Resch, A. Schulz, and W. E. Nagel (Springer, Berlin, 2010), pp. 17{30.

[5] P. Saviankou, M. Knobloch, A. Visser, and B. Mohr, Cube v4: From performance report explorer to performance analysis tool, Procedia Computer Science 51, 1343{1352 (2015).

[6] S. S. Shende and A. D. Malony, The TAU parallel performance system, International Journal of High Performance Computing Applications 20, 287{311 (2006).

[7] M. Gerndt, E. Cesar, and S. Benkner (eds.), Automatic Tuning of HPC Applications - The Periscope Tuning Framework (Shaker Verlag, Aachen, 2015).

[8] A. Knupfer, C. Rossel, D. an Mey, S. Biersdorff, K. Diethelm, D. Eschweiler, M. Geimer, M. Gerndt, D. Lorenz, A. D. Malony, W. E. Nagel, Y. Oleynik, P. Philippen, P. Saviankou, D. Schmidl, S. S. Shende, R. Tschuter, M. Wagner, B. Wesarg, and F. Wolf, Score-P: A joint performance measurement run-time infrastructure for Periscope, Scalasca, TAU, and Vampir, in: Tools for High Performance Computing 2011, edited by H. Brunst, M. Muller, W. E. Nagel, and M. M. Resch (Springer, Berlin, 2012), pp. 79{91.

[10] Y. Oleynik, M. Gerndt, J. Schuchart, P. G. Kjeldsberg, and W. E. Nagel, Run-time exploitation of application dynamism for energye cient exascale computing (READEX), in: Computational Science and Engineering (CSE), 2015 IEEE 18th International Conference on, edited by C. Plessl, D. El Baz, G. Cong, J. M. P. Cardoso, L. Veiga, and T. Rauber (IEEE, Piscataway, 2015), pp. 347{350. [OpenAIRE]

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Abstract
Score-P is a measurement infrastructure originally designed for the analysis and optimization of the performance of HPC codes. Recent extensions of Score-P and its associated tools now also allow the investigation of energy-related properties and support the user in the implementation of corresponding improvements. Since it would be counterproductive to completely ignore performance issues in this connection, the focus should not be laid exclusively on energy. We therefore aim to optimize software with respect to an objective function that takes into account energy and run time.
doi: 10.1002/pamm.201610407
Subjects
free text keywords: D.2.8, C.1.4, C.4, 68M20, 68N30, 68W40, Computer Science - Software Engineering, Software, business.industry, business, Systems engineering, Energy requirement, Computer science
Funded by
EC| READEX
Project
READEX
Runtime Exploitation of Application Dynamism for Energy-efficient eXascale computing
  • Funder: European Commission (EC)
  • Project Code: 671657
  • Funding stream: H2020 | RIA
Communities
FET H2020FET HPC: HPC Core Technologies, Programming Environments and Algorithms for Extreme Parallelism and Extreme Data Applications
FET H2020FET HPC: Runtime Exploitation of Application Dynamism for Energy-efficient eXascale computing
Download fromView all 4 versions
OpenAIRE
Article . 2016
Provider: OpenAIRE
PAMM
Article
Provider: UnpayWall
arXiv.org e-Print Archive
Preprint . 2016
Provider: arXiv.org e-Print Archive
http://dx.doi.org/10.1002/pamm...
Article
Provider: Sygma
View more

[1] C. Bischof, D. an Mey, and C. Iwainsky, Brainware for green HPC, Computer Science | Research and Development 27, 227{233 (2012).

[2] Leibniz-Rechenzentrum der Bayerischen Akademie der Wissenschaften, Working with energy aware jobs on SuperMUC, https: //www.lrz.de/services/compute/supermuc/loadleveler/#energy.

[3] I. Zhukov, C. Feld, M. Geimer, M. Knobloch, B. Mohr, and P. Saviankou, Scalasca v2: Back to the future, in: Tools for High Performance Computing 2014, edited by C. Niethammer, J. Gracia, A. Knupfer, M. M. Resch, and W. E. Nagel (Springer, Cham, 2015), pp. 1{24.

[4] H. Brunst, D. Hackenberg, G. Juckeland, and H. Rohling, Comprehensive performance tracking with Vampir 7, in: Tools for High Performance Computing, edited by M. S. Muller, M. M. Resch, A. Schulz, and W. E. Nagel (Springer, Berlin, 2010), pp. 17{30.

[5] P. Saviankou, M. Knobloch, A. Visser, and B. Mohr, Cube v4: From performance report explorer to performance analysis tool, Procedia Computer Science 51, 1343{1352 (2015).

[6] S. S. Shende and A. D. Malony, The TAU parallel performance system, International Journal of High Performance Computing Applications 20, 287{311 (2006).

[7] M. Gerndt, E. Cesar, and S. Benkner (eds.), Automatic Tuning of HPC Applications - The Periscope Tuning Framework (Shaker Verlag, Aachen, 2015).

[8] A. Knupfer, C. Rossel, D. an Mey, S. Biersdorff, K. Diethelm, D. Eschweiler, M. Geimer, M. Gerndt, D. Lorenz, A. D. Malony, W. E. Nagel, Y. Oleynik, P. Philippen, P. Saviankou, D. Schmidl, S. S. Shende, R. Tschuter, M. Wagner, B. Wesarg, and F. Wolf, Score-P: A joint performance measurement run-time infrastructure for Periscope, Scalasca, TAU, and Vampir, in: Tools for High Performance Computing 2011, edited by H. Brunst, M. Muller, W. E. Nagel, and M. M. Resch (Springer, Berlin, 2012), pp. 79{91.

[10] Y. Oleynik, M. Gerndt, J. Schuchart, P. G. Kjeldsberg, and W. E. Nagel, Run-time exploitation of application dynamism for energye cient exascale computing (READEX), in: Computational Science and Engineering (CSE), 2015 IEEE 18th International Conference on, edited by C. Plessl, D. El Baz, G. Cong, J. M. P. Cardoso, L. Veiga, and T. Rauber (IEEE, Piscataway, 2015), pp. 347{350. [OpenAIRE]

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