Prediction models for multi-dimensional power-performance optimization on many cores

  • Authors:

  • Matthew Curtis-Maury;Ankur Shah;Filip Blagojevic;Dimitrios S. Nikolopoulos;Bronis R. de Supinski;Martin Schulz

  • Affiliations:

  • Virginia Tech, Blacksburg, VA, USA;Virginia Tech, Blacksburg, VA, USA;Virginia Tech, Blacksburg, VA, USA;Virginia Tech, Blacksburg, VA, USA;Lawrence Livermore National Laboratory, Livermore, CA, USA;Lawrence Livermore National Laboratory, Livermore, CA, USA

  • Venue:
  • Proceedings of the 17th international conference on Parallel architectures and compilation techniques
  • Year:
  • 2008

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Abstract

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.