| 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 |
|
| Keywords |
- concurrency throttling
- power-aware computing
- runtime adaptation
- performance prediction
- high-performance computing
- Multicore processors
|
| Date of Defense |
2008-04-18 |
| Availability |
unrestricted |
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 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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| Files |
| Filename |
Size |
Approximate Download Time
(Hours:Minutes:Seconds)
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56K Modem |
ISDN (64 Kb) |
ISDN (128 Kb) |
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Ankur_Thesis.pdf |
2.31 Mb |
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