Can Deng, Zhaoyun Chen, Yang Shi, Xichang Kong, Mei Wen
{"title":"Exploring ILP for VLIW Architecture by Quantified Modeling and Dynamic Programming-Based Instruction Scheduling","authors":"Can Deng, Zhaoyun Chen, Yang Shi, Xichang Kong, Mei Wen","doi":"10.1109/ASP-DAC52403.2022.9712500","DOIUrl":null,"url":null,"abstract":"Exploring the instruction-level parallelism (ILP) of Very Long Instruction Word (VLIW) architecture relies on instruction scheduling. List scheduling (LS) algorithms, which are most adopted in modern compilers, have limitations in searching for optimal solutions. This paper proposes a quantifiable model for instruction scheduling and a dynamic programming-based strategy (DPS). We evaluate DPS on a specified platform and realize high efficiency. The results suggest that the DPS achieves an efficiency improvement of up to 44.72% within acceptable time overhead.","PeriodicalId":239260,"journal":{"name":"2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASP-DAC52403.2022.9712500","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Exploring the instruction-level parallelism (ILP) of Very Long Instruction Word (VLIW) architecture relies on instruction scheduling. List scheduling (LS) algorithms, which are most adopted in modern compilers, have limitations in searching for optimal solutions. This paper proposes a quantifiable model for instruction scheduling and a dynamic programming-based strategy (DPS). We evaluate DPS on a specified platform and realize high efficiency. The results suggest that the DPS achieves an efficiency improvement of up to 44.72% within acceptable time overhead.