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Design-time Analysis for the READEX Tool Suite
EC| READEX
M. Kumaraswamy, A. Chowdhury, M. Gerndt;
M. Kumaraswamy, A. Chowdhury, M. Gerndt;
Design-time Analysis for the READEX Tool Suite
Energy efficiency and consumption are now the most important and challenging issues in current Petascale and in designing future Exascale computing systems. The European Union Horizon 2020 READEX project uses an online approach to exploit application dynamism and tune large-scale HPC applications to improve energy efficiency and performance. The paper presents the READEX methodology, consisting of the Design-Time Analysis and Runtime Application Tuning, and describes the pre-analysis steps involving application dynamism and significant region detection. During design-time, the READEX tuning plugin evaluates configurations of hardware and software tuning parameters to determine the best settings for instances of application regions. The runtime tuning dynamically switches to the best configuration for an application region during production runs. Finally, the energy savings obtained for LULESH on the Taurus supercomputer highlight the effectiveness of this methodology.
Dewey Decimal Classification: ddc:000
Informatik, Wissen, Systeme
Informatik, Wissen, Systeme
Dewey Decimal Classification: ddc:000
1312
[1] Y. Oleynik, M. Gerndt, J. Schuchart, P. G. Kjeldsberg, and W. E. Nagel, “Run-time exploitation of application dynamism for energy-efficient exascale computing (READEX),” in Computational Science and Engineering (CSE), 2015 IEEE 18th International Conference on, C. Plessl, D. El Baz, G. Cong, J. M. P. Cardoso, L. Veiga, and T. Rauber, Eds. Piscataway: IEEE, Oct 2015, pp. 347-350. [OpenAIRE]
[2] A. Knu¨pfer, C. Ro¨ssel, 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. Tschu¨ter, 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, H. Brunst, M. Mu¨ller, W. E. Nagel, and M. M. Resch, Eds. Berlin: Springer, 2012, pp. 79-91.
[3] “Score-E: Scalable tools for the analysis and optimization of energy consumption in HPC,” http://www. vi-hps.org/projects/score-e.
[4] D. Hackenberg, T. Ilsche, J. Schuchart, R. Scho¨ne, W. Nagel, M. Simon, and Y. Georgiou, “HDEEM: High Definition Energy Efficiency Monitoring,” in Energy Efficient Supercomputing Workshop (E2SC), Nov 2014, http://dx.doi.org/10.1109/E2SC.2014.13DOI: 10.1109/E2SC.2014.13.
[5] I. Karlin, J. Keasler, and R. Neely, “Lulesh 2.0 updates and changes,” Tech. Rep. LLNL-TR-641973, August 2013.
[6] M. Gerndt, E. Ce´sar, and S. Benkner, Eds., Automatic Tuning of HPC Applications - The Periscope Tuning Framework. Aachen: Shaker Verlag, 2015.
[7] C. Guillen, C. Navarrete, D. Brayford, W. Hesse, and M. Brehm, “Dvfs automatic tuning plugin for energy related tuning objectives,” in Green High Performance Computing (ICGHPC), 2016 2nd International Conference on. IEEE, 2016, pp. 1-8.
[8] J. H. Laros III, K. T. Pedretti, S. M. Kelly, W. Shu, and C. T. Vaughan, “Energy based performance tuning for large scale high performance computing systems,” in Proceedings of the 2012 Symposium on High Performance Computing. Society for Computer Simulation International, 2012, p. 6.
[9] M. Etinski, J. Corbala´n, J. Labarta, and M. Valero, “Understanding the future of energy-performance trade-off via dvfs in hpc environments,” Journal of Parallel and Distributed Computing, vol. 72, no. 4, pp. 579-590, 2012.
[10] H. Kimura, T. Imada, and M. Sato, “Runtime energy adaptation with low-impact instrumented code in a power-scalable cluster system,” in Cluster, Cloud and Grid Computing (CCGrid), 2010 10th IEEE/ACM International Conference on. IEEE, 2010, pp. 378-387.
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citations provided by BIP!popularityPopularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
Popularity provided by BIP!influenceInfluence provided by BIP!
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Influence provided by BIP!impulseImpulse provided by BIP!
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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Energy efficiency and consumption are now the most important and challenging issues in current Petascale and in designing future Exascale computing systems. The European Union Horizon 2020 READEX project uses an online approach to exploit application dynamism and tune large-scale HPC applications to improve energy efficiency and performance. The paper presents the READEX methodology, consisting of the Design-Time Analysis and Runtime Application Tuning, and describes the pre-analysis steps involving application dynamism and significant region detection. During design-time, the READEX tuning plugin evaluates configurations of hardware and software tuning parameters to determine the best settings for instances of application regions. The runtime tuning dynamically switches to the best configuration for an application region during production runs. Finally, the energy savings obtained for LULESH on the Taurus supercomputer highlight the effectiveness of this methodology.