Investigating power capping toward energy-efficient scientific applications: Investigating Power Capping toward Energy-Efficient Scientific Applications

Haidar, Azzam; Jagode, Heike; Vaccaro, Phil; YarKhan, Asim; Tomov, Stanimire; Dongarra, Jack

doi:10.1002/cpe.4485

Title: Investigating power capping toward energy-efficient scientific applications: Investigating Power Capping toward Energy-Efficient Scientific Applications

Journal Article · 25 March 2018 · Concurrency and Computation. Practice and Experience

DOI: https://doi.org/10.1002/cpe.4485 · OSTI ID:1435180

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^[1]; Vaccaro, Phil ^[1]; YarKhan, Asim ^[1]; Tomov, Stanimire ^[1];

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Univ. of Tennessee, Knoxville, TN (United States). Innovative Computing Lab
Univ. of Tennessee, Knoxville, TN (United States). Innovative Computing Lab ; Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Manchester (United Kingdom)

The emergence of power efficiency as a primary constraint in processor and system design poses new challenges concerning power and energy awareness for numerical libraries and scientific applications. Power consumption also plays a major role in the design of data centers, which may house petascale or exascale-level computing systems. At these extreme scales, understanding and improving the energy efficiency of numerical libraries and their related applications becomes a crucial part of the successful implementation and operation of the computing system. In this paper, we study and investigate the practice of controlling a compute system's power usage, and we explore how different power caps affect the performance of numerical algorithms with different computational intensities. Further, we determine the impact, in terms of performance and energy usage, that these caps have on a system running scientific applications. This analysis will enable us to characterize the types of algorithms that benefit most from these power management schemes. Our experiments are performed using a set of representative kernels and several popular scientific benchmarks. Lastly, we quantify a number of power and performance measurements and draw observations and conclusions that can be viewed as a roadmap to achieving energy efficiency in the design and execution of scientific algorithms.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1435180

Journal Information:: Concurrency and Computation. Practice and Experience, Vol. 31, Issue 6; ISSN 1532-0626

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

References (22)

PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications Ge, Rong; Feng, Xizhou; Song, Shuaiwen IEEE Transactions on Parallel and Distributed Systems, Vol. 21, Issue 5 https://doi.org/10.1109/TPDS.2009.76	journal	May 2010
Vampir Brunst, Holger; Knüpfer, Andreas; Salapura, Valentina Encyclopedia of Parallel Computing https://doi.org/10.1007/978-0-387-09766-4_60	book	January 2011
Improving Energy Efficiency in Memory-constrained Applications Using Core-specific Power Control Bhalachandra, Sridutt; Porterfield, Allan; Olivier, Stephen L. Proceedings of the 5th International Workshop on Energy Efficient Supercomputing - E2SC'17 https://doi.org/10.1145/3149412.3149418	conference	January 2017
The Tau Parallel Performance System Shende, Sameer S.; Malony, Allen D. The International Journal of High Performance Computing Applications, Vol. 20, Issue 2 https://doi.org/10.1177/1094342006064482	journal	May 2006
Run-Time Exploitation of Application Dynamism for Energy-Efficient Exascale Computing (READEX) Oleynik, Yury; Gerndt, Michael; Schuchart, Joseph 2015 IEEE 18th International Conference on Computational Science and Engineering (CSE) https://doi.org/10.1109/CSE.2015.55	conference	October 2015
SPEC CPU2006 benchmark descriptions Henning, John L. ACM SIGARCH Computer Architecture News, Vol. 34, Issue 4 https://doi.org/10.1145/1186736.1186737	journal	September 2006
Maximizing Performance Under a Power Cap: A Comparison of Hardware, Software, and Hybrid Techniques Zhang, Huazhe; Hoffmann, Henry ACM SIGPLAN Notices, Vol. 51, Issue 4 https://doi.org/10.1145/2954679.2872375	journal	June 2016
High-performance conjugate-gradient benchmark: A new metric for ranking high-performance computing systems Dongarra, Jack; Heroux, Michael A.; Luszczek, Piotr The International Journal of High Performance Computing Applications, Vol. 30, Issue 1 https://doi.org/10.1177/1094342015593158	journal	August 2015
Maximizing Performance Under a Power Cap: A Comparison of Hardware, Software, and Hybrid Techniques Zhang, Huazhe; Hoffmann, Henry Proceedings of the Twenty-First International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS '16 https://doi.org/10.1145/2872362.2872375	conference	January 2016
Power-Management Architecture of the Intel Microarchitecture Code-Named Sandy Bridge Rotem, Efraim; Naveh, Alon; Ananthakrishnan, Avinash IEEE Micro, Vol. 32, Issue 2 https://doi.org/10.1109/MM.2012.12	journal	March 2012
Adagio: making DVS practical for complex HPC applications Rountree, Barry; Lownenthal, David K.; de Supinski, Bronis R. Proceedings of the 23rd international conference on Conference on Supercomputing - ICS '09 https://doi.org/10.1145/1542275.1542340	conference	January 2009
Emprical study on Reducing Energy of Parallel Programs using Slack Reclamation by DVFS in a Power-scalable High Performance Cluster Kimura, Hideaki; Sato, Mitsuhisa; Hotta, Yoshihiko 2006 IEEE International Conference on Cluster Computing https://doi.org/10.1109/CLUSTR.2006.311839	conference	September 2006
Power monitoring with PAPI for extreme scale architectures and dataflow-based programming models McCraw, Heike; Ralph, James; Danalis, Anthony 2014 IEEE International Conference On Cluster Computing (CLUSTER) https://doi.org/10.1109/CLUSTER.2014.6968672	conference	September 2014
The READEX formalism for automatic tuning for energy efficiency Schuchart, Joseph; Gerndt, Michael; Kjeldsberg, Per Gunnar Computing, Vol. 99, Issue 8 https://doi.org/10.1007/s00607-016-0532-7	journal	January 2017
PerfExpert: An Easy-to-Use Performance Diagnosis Tool for HPC Applications Burtscher, Martin; Kim, Byoung-Do; Diamond, Jeff 2010 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis https://doi.org/10.1109/SC.2010.41	conference	November 2010
Application Runtime Variability and Power Optimization for Exascale Computers Porterfield, Allan; Fowler, Rob; Bhalachandra, Sridutt Proceedings of the 5th International Workshop on Runtime and Operating Systems for Supercomputers - ROSS '15 https://doi.org/10.1145/2768405.2768408	conference	January 2015
Parallel Performance Measurement of Heterogeneous Parallel Systems with GPUs Malony, Allen D.; Biersdorff, Scott; Shende, Sameer 2011 International Conference on Parallel Processing (ICPP) https://doi.org/10.1109/ICPP.2011.71	conference	September 2011
Beyond the CPU: Hardware Performance Counter Monitoring on Blue Gene/Q McCraw, Heike; Terpstra, Dan; Dongarra, Jack Lecture Notes in Computer Science https://doi.org/10.1007/978-3-642-38750-0_16	book	January 2013
PAPI-V: Performance Monitoring for Virtual Machines Johnson, Matthew; McCraw, Heike; Moore, Shirley 2012 41st International Conference on Parallel Processing Workshops (ICPPW) https://doi.org/10.1109/ICPPW.2012.29	conference	September 2012
HPCTOOLKIT: tools for performance analysis of optimized parallel programs Adhianto, L.; Banerjee, S.; Fagan, M. Concurrency and Computation: Practice and Experience https://doi.org/10.1002/cpe.1553	journal	January 2009
The Scalasca performance toolset architecture Geimer, Markus; Wolf, Felix; Wylie, Brian J. N. Concurrency and Computation: Practice and Experience https://doi.org/10.1002/cpe.1556	journal	January 2010
Maximizing Performance Under a Power Cap: A Comparison of Hardware, Software, and Hybrid Techniques Zhang, Huazhe; Hoffmann, Henry ACM SIGOPS Operating Systems Review, Vol. 50, Issue 2 https://doi.org/10.1145/2954680.2872375	journal	March 2016

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MERIC and RADAR Generator: Tools for Energy Evaluation and Runtime Tuning of HPC Applications Vysocky, Ondrej; Beseda, Martin; Říha, Lubomír Lecture Notes in Computer Science https://doi.org/10.1007/978-3-319-97136-0_11	book	January 2018
Power-aware computing: Power-aware computing Ezzatti, Pablo; Quintana-Ortí, Enrique S.; Remón, Alfredo Concurrency and Computation: Practice and Experience, Vol. 31, Issue 6 https://doi.org/10.1002/cpe.5034	journal	November 2018
Energy-Aware High-Performance Computing: Survey of State-of-the-Art Tools, Techniques, and Environments Czarnul, Pawel; Proficz, Jerzy; Krzywaniak, Adam Scientific Programming, Vol. 2019 https://doi.org/10.1155/2019/8348791	journal	April 2019
Investigating power efficiency of mergesort Aljabri, Naif; Al-Hashimi, Muhammad; Saleh, Mostafa The Journal of Supercomputing, Vol. 75, Issue 10 https://doi.org/10.1007/s11227-019-02850-5	journal	April 2019

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Title: Investigating power capping toward energy-efficient scientific applications: Investigating Power Capping toward Energy-Efficient Scientific Applications

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