2008 International Conference on Parallel Architectures and Compilation Techniques (PACT) (2008)
Toronto, ON, Canada
Oct. 25, 2008 to Oct. 29, 2008
DOI Bookmark: http://doi.ieeecomputersociety.org/
Matthew Curtis-Maury , Dept. of Computer Science, Virginia Tech, Blacksburg, USA
Ankur Shah , Dept. of Computer Science, Virginia Tech, Blacksburg, USA
Filip Blagojevic , Dept. of Computer Science, Virginia Tech, Blacksburg, USA
Dimitrios S. Nikolopoulos , Dept. of Computer Science, Virginia Tech, Blacksburg, USA
Bronis R. de Supinski , Lawrence Livermore National Laboratory, CA, USA
Martin Schulz , Lawrence Livermore National Laboratory, CA, USA
Power has become a primary concern for HPC systems. Dynamic voltage and frequency scaling (DVFS) and dynamic concurrency throttling (DCT) are two software tools (or knobs) for reducing the dynamic power consumption of HPC systems. To date, few works have considered the synergistic integration of DVFS and DCT in performance-constrained systems, and, to the best of our knowledge, no prior research has developed application-aware simultaneous DVFS and DCT controllers in real systems and parallel programming frameworks. We present a multi-dimensional, online performance predictor, which we deploy to address the problem of simultaneous runtime optimization of DVFS and DCT on multi-core systems. We present results from an implementation of the predictor in a runtime library linked to the Intel OpenMP environment and running on an actual dual-processor quad-core system. We show that our predictor derives near-optimal settings of the power-aware program adaptation knobs that we consider. Our overall framework achieves significant reductions in energy (19% mean) and ED2 (40% mean), through simultaneous power savings (6% mean) and performance improvements (14% mean). We also find that our framework outperforms earlier solutions that adapt only DVFS or DCT, as well as one that sequentially applies DCT then DVFS. Further, our results indicate that prediction-based schemes for runtime adaptation compare favorably and typically improve upon heuristic search-based approaches in both performance and energy savings.
Predictive models, Runtime, Multicore processing, Mathematical model, Adaptation models, Concurrent computing, Hardware
M. Curtis-Maury, A. Shah, F. Blagojevic, D. S. Nikolopoulos, B. R. de Supinski and M. Schulz, "Prediction models for multi-dimensional power-performance optimization on many cores," 2008 International Conference on Parallel Architectures and Compilation Techniques (PACT), Toronto, ON, Canada, 2008, pp. 250-259.