Energy Efficiency Modeling of Parallel Applications

Mark Endrei, Chao Jin, Minh Ngoc Dinh, David Abramson, Heidi Poxon, Luiz DeRose, Bronis R. de Supinski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


Energy efficiency has become increasingly important in high performance computing (HPC), as power constraints and costs escalate. Workload and system characteristics form a complex optimization search space in which optimal settings for energy efficiency and performance often diverge. Thus, we must identify trade-off options for performance and energy efficiency to find the desired balance between them. We present an innovative statistical model that accurately predicts the Pareto optimal performance and energy efficiency trade-off options using only user-controllable parameters. Our approach can also tolerate both measurement and model errors. We study model training and validation using several HPC kernels, then explore the feasibility of applying the model to more complex workloads, including AMG and LAMMPS. We can calibrate an accurate model from as few as 12 runs, with prediction error of less than 10%. Our results identify trade-off options allowing up to 40% improvement in energy efficiency at the cost of under 20% performance loss. For AMG, we reduce the required sample measurement time from 13 hours to 74 minutes (about 90%).

Original languageEnglish
Title of host publicationInternational Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2018): Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages13
ISBN (Electronic)9781538683842
ISBN (Print)978-1-5386-8385-9
Publication statusPublished - 14 Mar 2019
Event2018 International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018 - Dallas, United States
Duration: 11 Nov 201816 Nov 2018


Conference2018 International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Hardware and Architecture
  • Theoretical Computer Science


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