Issue No. 01 - Jan. (2013 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2012.95
Dong Li , Oak Ridge National Laboratory, Oak Ridge and Virginia Tech, Blacksburg
Bronis R. de Supinski , Lawrence Livermore National Lab, Livermore
Martin Schulz , Lawrence Livermore National Lab, Livermore
Dimitrios S. Nikolopoulos , Queen's University of Belfast and FORTH-ICS, Heraklion
Kirk W. Cameron , Virginia Tech, Blacksburg
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 percent on average and up to 13.8 percent) with some performance gain (up to 7.5 percent) or negligible performance loss.
Discrete cosine transforms, Concurrent computing, Programming, Computational modeling, Time frequency analysis, Dynamic programming, Multicore processing, dynamic voltage and frequency scaling, Power management, hybrid parallel programming models, dynamic concurrency throttling
M. Schulz, D. S. Nikolopoulos, B. R. de Supinski, K. W. Cameron and D. Li, "Strategies for Energy-Efficient Resource Management of Hybrid Programming Models," in IEEE Transactions on Parallel & Distributed Systems, vol. 24, no. , pp. 144-157, 2013.