Abstract
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 language | English |
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Title of host publication | International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2018): Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 212-224 |
Number of pages | 13 |
ISBN (Electronic) | 9781538683842 |
ISBN (Print) | 978-1-5386-8385-9 |
DOIs | |
Publication status | Published - 14 Mar 2019 |
Event | 2018 International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018 - Dallas, United States Duration: 11 Nov 2018 → 16 Nov 2018 |
Conference
Conference | 2018 International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018 |
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Country/Territory | United States |
City | Dallas |
Period | 11/11/2018 → 16/11/2018 |
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computer Networks and Communications
- Hardware and Architecture
- Theoretical Computer Science