Strategies for Energy Efficient Resource Management of Hybrid Programming Models
- ORNL
- Lawrence Livermore National Laboratory (LLNL)
- Virginia Polytechnic Institute and State University
Many scientific applications are programmed using hybrid programming models that use both message-passing and shared-memory, due to the increasing prevalence of large-scale systems with multicore, multisocket nodes. Previous work has shown that energy efficiency can be improved using software-controlled execution schemes that consider both the programming model and the power-aware execution capabilities of the system. However, such approaches have focused on identifying optimal resource utilization for one programming model, either shared-memory or message-passing, in isolation. The potential solution space, thus the challenge, increases substantially when optimizing hybrid models since the possible resource configurations increase exponentially. Nonetheless, with the accelerating adoption of hybrid programming models, we increasingly need improved energy efficiency in hybrid parallel applications on large-scale systems. In this work, we present new software-controlled execution schemes that consider the effects of dynamic concurrency throttling (DCT) and dynamic voltage and frequency scaling (DVFS) in the context of hybrid programming models. Specifically, we present predictive models and novel algorithms based on statistical analysis that anticipate application power and time requirements under different concurrency and frequency configurations. We apply our models and methods to the NPB MZ benchmarks and selected applications from the ASC Sequoia codes. Overall, we achieve substantial energy savings (8.74% on average and up to 13.8%) with some performance gain (up to 7.5%) or negligible performance loss.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- Work for Others (WFO); USDOE Office of Science (SC)
- DOE Contract Number:
- DE-AC05-00OR22725
- OSTI ID:
- 1048160
- Journal Information:
- IEEE Transactions on Parallel and Distributed Systems, Vol. 24, Issue 1
- Country of Publication:
- United States
- Language:
- English
Energy measurement, modeling, and prediction for processors with frequency scaling
|
journal | June 2014 |
Energy-Aware High-Performance Computing: Survey of State-of-the-Art Tools, Techniques, and Environments
|
journal | April 2019 |
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