{"title":"长期FPGA资源管理的时空策略","authors":"Atefeh Mehrabi, Daniel J. Sorin, Benjamin C. Lee","doi":"10.1109/ispass55109.2022.00026","DOIUrl":null,"url":null,"abstract":"The deployment of increasingly large and capable FPGAs has motivated mechanisms for sharing them, but system support for FPGAs is not yet mature. Traditional scheduling algorithms do not account for the unique characteristics of FPGAs, leading to infeasible or inefficient allocations. We propose a novel scheduling policy, called Spatiotemporal FPGA Scheduling, that overcomes these challenges to achieve long-term target allocations by tracking and correcting deviations from targets across management time periods. Compared to traditional algorithms, Spatiotemporal FPGA Scheduling produces allocations that are up to 32% closer to targets, improves average throughput by up to 44%, and improves average FPGA utilization by up to 23%.","PeriodicalId":115391,"journal":{"name":"2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatiotemporal Strategies for Long-Term FPGA Resource Management\",\"authors\":\"Atefeh Mehrabi, Daniel J. Sorin, Benjamin C. Lee\",\"doi\":\"10.1109/ispass55109.2022.00026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The deployment of increasingly large and capable FPGAs has motivated mechanisms for sharing them, but system support for FPGAs is not yet mature. Traditional scheduling algorithms do not account for the unique characteristics of FPGAs, leading to infeasible or inefficient allocations. We propose a novel scheduling policy, called Spatiotemporal FPGA Scheduling, that overcomes these challenges to achieve long-term target allocations by tracking and correcting deviations from targets across management time periods. Compared to traditional algorithms, Spatiotemporal FPGA Scheduling produces allocations that are up to 32% closer to targets, improves average throughput by up to 44%, and improves average FPGA utilization by up to 23%.\",\"PeriodicalId\":115391,\"journal\":{\"name\":\"2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ispass55109.2022.00026\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ispass55109.2022.00026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Spatiotemporal Strategies for Long-Term FPGA Resource Management
The deployment of increasingly large and capable FPGAs has motivated mechanisms for sharing them, but system support for FPGAs is not yet mature. Traditional scheduling algorithms do not account for the unique characteristics of FPGAs, leading to infeasible or inefficient allocations. We propose a novel scheduling policy, called Spatiotemporal FPGA Scheduling, that overcomes these challenges to achieve long-term target allocations by tracking and correcting deviations from targets across management time periods. Compared to traditional algorithms, Spatiotemporal FPGA Scheduling produces allocations that are up to 32% closer to targets, improves average throughput by up to 44%, and improves average FPGA utilization by up to 23%.