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- Hao, MengSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China (author)
Fine-grained Powercap Allocation for Power-constrained Systems based on Multi-objective Machine Learning
- Article/chapterEnglish2021
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- IEEE,2021
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- LIBRIS-ID:oai:DiVA.org:ltu-82362
- https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-82362URI
- https://doi.org/10.1109/TPDS.2020.3045983DOI
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- Language:English
- Summary in:English
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- Validerad;2021;Nivå 2;2021-02-22 (johcin);Finansiär: National Key Research and Development Program of China (2017YFB0202901), Key-Area Research and Development Program of Guangdong Province (2019B010136001), National Natural Science Foundation of China (61672186), Shenzhen Science and Technology Research and Development Foundation (JCYJ20190806143418198)
- Power capping is an important solution to keep the system within a fixed power constraint. However, for the power-constrained systems, powercap needs to be reasonably allocated according to the workloads of compute nodes to achieve trade-offs among performance, energy and powercap. Thus it is necessary to model performance and energy and to predict the optimal powercap allocation strategies. Existing power allocation approaches have insufficient granularity within nodes. Modeling approaches usually model performance and energy separately, ignoring the correlation between objectives, and do not expose the Pareto-optimal powercap configurations. Therefore, this paper proposes an approach to predict the Pareto-optimal powercap configurations on the power-constrained system for MPI and OpenMP parallel applications. It uses the elaboratly designed micro-benchmarks and a small number of existing benchmarks to build the training set, then applies a multi-objective machine learning algorithm which combines the stacked single-target with extreme gradient boosting to build multi-objective models. The models can be used to predict the optimal PKG and DRAM powercap settings, helping compute nodes perform fine-grained powercap allocation. Compared with the reference configuration, our models can achieve an average powercap reduction of up to 48%, average energy reduction of up to around 20%, with only 10%~30% maximum performance drop.
Subject headings and genre
- NATURVETENSKAP Data- och informationsvetenskap Medieteknik hsv//swe
- NATURAL SCIENCES Computer and Information Sciences Media and Communication Technology hsv//eng
- Power capping
- Performance and energy modeling
- Pareto front
- Multi-objective machine learning
- Pervasive Mobile Computing
- Distribuerade datorsystem
Added entries (persons, corporate bodies, meetings, titles ...)
- Zhang, WeizheSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China. Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen, China (author)
- Wang, YimingSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China (author)
- Lu, GangzhaoSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China (author)
- Wang, FaruiSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China (author)
- Vasilakos, Athanasios V.Luleå tekniska universitet,Datavetenskap,School of Electrical and Data Engineering, University Technology Sydney, Australia. e Department of Computer Science and Technology, Fuzhou University, Fuzhou 350116, China(Swepub:ltu)thanos (author)
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, ChinaSchool of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China. Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen, China (creator_code:org_t)
Related titles
- In:IEEE Transactions on Parallel and Distributed Systems: IEEE32:7, s. 1789-18011045-92191558-2183
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