{"title":"一种自适应无阻塞GVT算法","authors":"Eric Mikida, L. Kalé","doi":"10.1145/3316480.3322896","DOIUrl":null,"url":null,"abstract":"In optimistic Parallel Discrete Event Simulations (PDES), the Global Virtual Time (GVT) computation is an important aspect of performance. It must be performed frequently enough to ensure simulation progress and free memory, while still incurring minimal overhead. Many algorithms have been studied for computing the GVT efficiently under a variety of simulation conditions for a variety of models. In this paper we propose a new GVT algorithm which aims to do two things. First, it incurs a very low overhead on the simulation by not requiring the simulation to block execution. Secondly, and most importantly, it has the ability to adapt to simulation conditions while it's running. This allows it to perform well for a variety of models, and helps remove some burden from developers by not requiring intensive tuning.","PeriodicalId":398793,"journal":{"name":"Proceedings of the 2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","volume":"87 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"An Adaptive Non-Blocking GVT Algorithm\",\"authors\":\"Eric Mikida, L. Kalé\",\"doi\":\"10.1145/3316480.3322896\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In optimistic Parallel Discrete Event Simulations (PDES), the Global Virtual Time (GVT) computation is an important aspect of performance. It must be performed frequently enough to ensure simulation progress and free memory, while still incurring minimal overhead. Many algorithms have been studied for computing the GVT efficiently under a variety of simulation conditions for a variety of models. In this paper we propose a new GVT algorithm which aims to do two things. First, it incurs a very low overhead on the simulation by not requiring the simulation to block execution. Secondly, and most importantly, it has the ability to adapt to simulation conditions while it's running. This allows it to perform well for a variety of models, and helps remove some burden from developers by not requiring intensive tuning.\",\"PeriodicalId\":398793,\"journal\":{\"name\":\"Proceedings of the 2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation\",\"volume\":\"87 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3316480.3322896\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3316480.3322896","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In optimistic Parallel Discrete Event Simulations (PDES), the Global Virtual Time (GVT) computation is an important aspect of performance. It must be performed frequently enough to ensure simulation progress and free memory, while still incurring minimal overhead. Many algorithms have been studied for computing the GVT efficiently under a variety of simulation conditions for a variety of models. In this paper we propose a new GVT algorithm which aims to do two things. First, it incurs a very low overhead on the simulation by not requiring the simulation to block execution. Secondly, and most importantly, it has the ability to adapt to simulation conditions while it's running. This allows it to perform well for a variety of models, and helps remove some burden from developers by not requiring intensive tuning.