Title page for ETD etd-04222008-133454

Type of Document Master's Thesis
Author Shah, Ankur Savailal
Author's Email Address ankur77@vt.edu
URN etd-04222008-133454
Title Prediction Models for Multi-dimensional Power-Performance Optimization on Many Cores
Degree Master of Science
Department Computer Science
Advisory Committee
Advisor Name Title
Nikolopoulos, Dimitrios S. Committee Chair
Cameron, Kirk W. Committee Member
Feng, Wu-Chun Committee Member
  • concurrency throttling
  • power-aware computing
  • runtime adaptation
  • performance prediction
  • high-performance computing
  • Multicore processors
Date of Defense 2008-04-18
Availability unrestricted
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 prediction framework, which we deploy to address the problem of simultaneous runtime optimization of DVFS, DCT, and thread placement on multi-core systems. We present results from an implementation of the prediction framework in a runtime system linked to the Intel OpenMP runtime environment and running on a real dual-processor quad-core system as well as a dual-processor dual-core system. We show that the prediction framework derives near-optimal settings of the three power-aware program adaptation knobs that we consider. Our overall runtime optimization framework achieves significant reductions in energy (12.27% mean) and ED2 (29.6% mean), through simultaneous power savings (3.9% mean) and performance improvements (10.3% mean). Our prediction and adaptation 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.

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