Xia Cai, Li Xu, C. Devon Lin, Yili Hong, Xinwei Deng
{"title":"基于定量和定性因素的自适应区域序列设计在高性能计算机配置中的应用","authors":"Xia Cai, Li Xu, C. Devon Lin, Yili Hong, Xinwei Deng","doi":"10.1080/00224065.2023.2241680","DOIUrl":null,"url":null,"abstract":"AbstractMotivated by the need of finding optimal configuration in the high-performance computing (HPC) system, this work proposes an adaptive-region sequential design (ARSD) for optimization of computer experiments with qualitative and quantitative factors. Experiments with both qualitative and quantitative factors are also encountered in other applications. The proposed ARSD method considers a sequential design criterion under the additive Gaussian process to deal with both qualitative and quantitative factors. Moreover, the adaptiveness of the proposed sequential procedure allows the selection of next design point from the adaptive design region achieving a meaningful balance between exploitation and exploration for optimization. Theoretical justification of the adaptive design region is provided. The performance of the proposed method is evaluated by several numerical examples in simulations. The case study of HPC performance optimization further elaborates the merits of the proposed method.Keywords: Adaptive designdesign of experimentexploitation and explorationGaussian processoptimal configuration AcknowledgementWe are grateful to the editor and the referees for their constructive comments that have helped improve the article significantly.Data availability statementThe data that support the findings of this study are available from the corresponding author upon reasonable request.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe work by Cai is supported by the National Natural Science Foundation of China (Grant No. 12001155), and the Natural Science Foundation of Hebei Province of China (Grant No. A2022208001). The work by Lin is supported by the Natural Sciences and Engineering Research Council of Canada.Notes on contributorsXia CaiXia Cai is an Associate Professor in the School of Science at the Hebei University of Science and Technology. Her email address is caixia@hebust.edu.cn.Li XuLi Xu is a PhD candidate in the Department of Statistics at the Virginia Tech. His email address is lix1992@vt.edu.C. Devon LinChunfang Devon Lin is a Professor in the Department of Mathematics and Statistics at the Queen’s University. Her email address is devon.lin@queensu.caYili HongYili Hong is a Professor in the Department of Statistics at the Virginia Tech. His email address is yilihong@vt.edu.Xinwei DengXinwei Deng is a Professor in the Department of Statistics at the Virginia Tech. His email address is xdeng@vt.edu.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":"78 1","pages":"0"},"PeriodicalIF":2.6000,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptive-region sequential design with quantitative and qualitative factors in application to HPC configuration\",\"authors\":\"Xia Cai, Li Xu, C. Devon Lin, Yili Hong, Xinwei Deng\",\"doi\":\"10.1080/00224065.2023.2241680\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"AbstractMotivated by the need of finding optimal configuration in the high-performance computing (HPC) system, this work proposes an adaptive-region sequential design (ARSD) for optimization of computer experiments with qualitative and quantitative factors. Experiments with both qualitative and quantitative factors are also encountered in other applications. The proposed ARSD method considers a sequential design criterion under the additive Gaussian process to deal with both qualitative and quantitative factors. Moreover, the adaptiveness of the proposed sequential procedure allows the selection of next design point from the adaptive design region achieving a meaningful balance between exploitation and exploration for optimization. Theoretical justification of the adaptive design region is provided. The performance of the proposed method is evaluated by several numerical examples in simulations. The case study of HPC performance optimization further elaborates the merits of the proposed method.Keywords: Adaptive designdesign of experimentexploitation and explorationGaussian processoptimal configuration AcknowledgementWe are grateful to the editor and the referees for their constructive comments that have helped improve the article significantly.Data availability statementThe data that support the findings of this study are available from the corresponding author upon reasonable request.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe work by Cai is supported by the National Natural Science Foundation of China (Grant No. 12001155), and the Natural Science Foundation of Hebei Province of China (Grant No. A2022208001). The work by Lin is supported by the Natural Sciences and Engineering Research Council of Canada.Notes on contributorsXia CaiXia Cai is an Associate Professor in the School of Science at the Hebei University of Science and Technology. Her email address is caixia@hebust.edu.cn.Li XuLi Xu is a PhD candidate in the Department of Statistics at the Virginia Tech. His email address is lix1992@vt.edu.C. Devon LinChunfang Devon Lin is a Professor in the Department of Mathematics and Statistics at the Queen’s University. Her email address is devon.lin@queensu.caYili HongYili Hong is a Professor in the Department of Statistics at the Virginia Tech. His email address is yilihong@vt.edu.Xinwei DengXinwei Deng is a Professor in the Department of Statistics at the Virginia Tech. 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Adaptive-region sequential design with quantitative and qualitative factors in application to HPC configuration
AbstractMotivated by the need of finding optimal configuration in the high-performance computing (HPC) system, this work proposes an adaptive-region sequential design (ARSD) for optimization of computer experiments with qualitative and quantitative factors. Experiments with both qualitative and quantitative factors are also encountered in other applications. The proposed ARSD method considers a sequential design criterion under the additive Gaussian process to deal with both qualitative and quantitative factors. Moreover, the adaptiveness of the proposed sequential procedure allows the selection of next design point from the adaptive design region achieving a meaningful balance between exploitation and exploration for optimization. Theoretical justification of the adaptive design region is provided. The performance of the proposed method is evaluated by several numerical examples in simulations. The case study of HPC performance optimization further elaborates the merits of the proposed method.Keywords: Adaptive designdesign of experimentexploitation and explorationGaussian processoptimal configuration AcknowledgementWe are grateful to the editor and the referees for their constructive comments that have helped improve the article significantly.Data availability statementThe data that support the findings of this study are available from the corresponding author upon reasonable request.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe work by Cai is supported by the National Natural Science Foundation of China (Grant No. 12001155), and the Natural Science Foundation of Hebei Province of China (Grant No. A2022208001). The work by Lin is supported by the Natural Sciences and Engineering Research Council of Canada.Notes on contributorsXia CaiXia Cai is an Associate Professor in the School of Science at the Hebei University of Science and Technology. Her email address is caixia@hebust.edu.cn.Li XuLi Xu is a PhD candidate in the Department of Statistics at the Virginia Tech. His email address is lix1992@vt.edu.C. Devon LinChunfang Devon Lin is a Professor in the Department of Mathematics and Statistics at the Queen’s University. Her email address is devon.lin@queensu.caYili HongYili Hong is a Professor in the Department of Statistics at the Virginia Tech. His email address is yilihong@vt.edu.Xinwei DengXinwei Deng is a Professor in the Department of Statistics at the Virginia Tech. His email address is xdeng@vt.edu.
期刊介绍:
The objective of Journal of Quality Technology is to contribute to the technical advancement of the field of quality technology by publishing papers that emphasize the practical applicability of new techniques, instructive examples of the operation of existing techniques and results of historical researches. Expository, review, and tutorial papers are also acceptable if they are written in a style suitable for practicing engineers.
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