{"title":"如何将CUDA-WSat-PcL加速5倍","authors":"Heng Liu, Arrvindh Shriraman, Evgenia Ternovska","doi":"10.1109/CANDAR.2016.0087","DOIUrl":null,"url":null,"abstract":"The Propositional Satisfiability Problem (SAT) is one of the most fundamental NP-complete problems, and is central to many domains of computer science. Utilizing a massively parallel architecture on a Graphics Processing Unit (GPU) together with a conventional CPU on NVIDIA's Compute Unified Device Architecture (CUDA) platform, this work proposes an efficient scheme to implement one parallel Stochastic Local Search (SLS) algorithms for SAT: CUDA-WSat-PcL. The implementation leads up to 5x speedup over the latest implementation of CUDA-WSat-PcL on CUDA. Additionally, our profiling results show that the CUDA portion of the new implementation is now at least 6x faster.","PeriodicalId":322499,"journal":{"name":"2016 Fourth International Symposium on Computing and Networking (CANDAR)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"How to Speed Up CUDA-WSat-PcL by 5x\",\"authors\":\"Heng Liu, Arrvindh Shriraman, Evgenia Ternovska\",\"doi\":\"10.1109/CANDAR.2016.0087\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Propositional Satisfiability Problem (SAT) is one of the most fundamental NP-complete problems, and is central to many domains of computer science. Utilizing a massively parallel architecture on a Graphics Processing Unit (GPU) together with a conventional CPU on NVIDIA's Compute Unified Device Architecture (CUDA) platform, this work proposes an efficient scheme to implement one parallel Stochastic Local Search (SLS) algorithms for SAT: CUDA-WSat-PcL. The implementation leads up to 5x speedup over the latest implementation of CUDA-WSat-PcL on CUDA. Additionally, our profiling results show that the CUDA portion of the new implementation is now at least 6x faster.\",\"PeriodicalId\":322499,\"journal\":{\"name\":\"2016 Fourth International Symposium on Computing and Networking (CANDAR)\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 Fourth International Symposium on Computing and Networking (CANDAR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CANDAR.2016.0087\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Fourth International Symposium on Computing and Networking (CANDAR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CANDAR.2016.0087","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Propositional Satisfiability Problem (SAT) is one of the most fundamental NP-complete problems, and is central to many domains of computer science. Utilizing a massively parallel architecture on a Graphics Processing Unit (GPU) together with a conventional CPU on NVIDIA's Compute Unified Device Architecture (CUDA) platform, this work proposes an efficient scheme to implement one parallel Stochastic Local Search (SLS) algorithms for SAT: CUDA-WSat-PcL. The implementation leads up to 5x speedup over the latest implementation of CUDA-WSat-PcL on CUDA. Additionally, our profiling results show that the CUDA portion of the new implementation is now at least 6x faster.
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