{"title":"Accelerating Energy Games Solvers on Modern Architectures","authors":"A. Formisano, R. Gentilini, Flavio Vella","doi":"10.1145/3149704.3149771","DOIUrl":null,"url":null,"abstract":"Quantitative games, where quantitative objectives are defined on weighted game arenas, provide natural tools for designing faithful models of embedded controllers. Instances of these games are the so called Energy Games. Starting from a sequential baseline implementation, we investigate the use of massively data computation capabilities supported by modern GPUs to solve the initial credit problem for Energy Games. We present different parallel implementations on multi-core CPU and GPU systems. Our solution outperforms the baseline implementation by up to 36x speedup and obtains a faster convergence time on real-world graphs.","PeriodicalId":292798,"journal":{"name":"Proceedings of the Seventh Workshop on Irregular Applications: Architectures and Algorithms","volume":"66 3","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Seventh Workshop on Irregular Applications: Architectures and Algorithms","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3149704.3149771","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Quantitative games, where quantitative objectives are defined on weighted game arenas, provide natural tools for designing faithful models of embedded controllers. Instances of these games are the so called Energy Games. Starting from a sequential baseline implementation, we investigate the use of massively data computation capabilities supported by modern GPUs to solve the initial credit problem for Energy Games. We present different parallel implementations on multi-core CPU and GPU systems. Our solution outperforms the baseline implementation by up to 36x speedup and obtains a faster convergence time on real-world graphs.
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