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Power-aware speedup (2007)
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| Venue: | In In Proceedings of the 21st IEEE International Parallel and Distributed Processing Symposium (IPDPS 07 |
| Citations: | 19 - 6 self |
BibTeX
@INPROCEEDINGS{Ge07power-awarespeedup,
author = {Rong Ge and Kirk Cameron},
title = {Power-aware speedup},
booktitle = {In In Proceedings of the 21st IEEE International Parallel and Distributed Processing Symposium (IPDPS 07},
year = {2007}
}
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Abstract
Power-aware processors operate in various power modes to reduce energy consumption with a corresponding decrease in peak processor throughput. Recent work has shown power-aware clusters can conserve significant energy (>30%) with minimal performance loss (<1%) running parallel scientific workloads. Nonetheless, such savings are typically achieved using a priori knowledge of application performance. Accurate prediction of parallel power consumption and performance is an open problem. However, such techniques would improve our understanding of power-aware cluster tradeoffs and enable identification of “sweet spots ” in system configurations optimized for performance and power. Speedup models are powerful analytical tools for evaluating and predicting the performance of parallel applications. Unfortunately, existing speedup models do not quantify parallel overhead for simplicity leaving them incapable of accurately accounting for performance and power. We propose power-aware speedup to model and predict the scaled execution time of power-aware clusters. The new model accounts for parallel overhead and predicts (within 7%) the power-aware performance and energy-delay products for various system configurations (i.e. processor counts and frequencies) on NAS Parallel benchmark codes. 1.
Keyphrases
power-aware speedup power-aware cluster speedup model parallel overhead corresponding decrease na parallel benchmark code various power mode various system configuration parallel application scaled execution time energy consumption new model account parallel scientific workload open problem minimal performance loss power-aware cluster tradeoff parallel power consumption recent work application performance sweet spot system configuration peak processor throughput power-aware performance priori knowledge energy-delay product enable identification accurate prediction processor count power-aware processor powerful analytical tool significant energy
