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Abstract
In the paper we tackle bi-objective execution time and power consumption optimization problem concerning execution of parallel applications. We propose using a discrete-event simulation environment for exploring this power/time trade-off in the form of a Pareto front. The solution is verified by a case study based on a real deep neural network training application for automatic speech recognition. A simulation lasting over 2 hours on a single CPU accurately predicts real results from executions that take over 335 hours in a cluster with 8 GPUs. The simulations allow also estimating the impact of data package imbalance on the application performance.
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- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.procs.2017.05.214
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- Category:
- Conference activity
- Type:
- materiały konferencyjne indeksowane w Web of Science
- Published in:
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Procedia Computer Science
no. 108,
pages 2463 - 2467,
ISSN: 1877-0509 - Title of issue:
- INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE (ICCS 2017) strony 2463 - 2467
- ISSN:
- 1877-0509
- Language:
- English
- Publication year:
- 2017
- Bibliographic description:
- Rościszewski P..: Modeling and Simulation for Exploring Power/Time Trade-off of Parallel Deep Neural Network Training, W: INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE (ICCS 2017), 2017, ,.
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.procs.2017.05.214
- Verified by:
- Gdańsk University of Technology
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