{"title":"模变量扩展敏感调度","authors":"M. Valluri, R. Govindarajan","doi":"10.1109/HIPC.1998.738006","DOIUrl":null,"url":null,"abstract":"Modulo scheduling is an aggressive scheduling technique for loops that exploit instruction-level parallelism by overlapping successive iterations of the loop. Due to the nature of modulo scheduling, the lifetime of a variable can overlap with a subsequent definition of itself. To handle such overlapping lifetimes, modulo-variable expansion (MVE) is used, wherein the constructed schedule is unrolled a number of times. We propose a technique to improve the constructed schedule while performing MVE. In our approach, we unroll the data dependence graph of the original loop and re-schedule it with a MVE-sensitive scheduler. Such an approach is expected to result in better initiation rates as compared to the traditional approach. We have implemented our approach and evaluated its performance on a large number of scientific benchmark kernels.","PeriodicalId":175528,"journal":{"name":"Proceedings. Fifth International Conference on High Performance Computing (Cat. No. 98EX238)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Modulo-variable expansion sensitive scheduling\",\"authors\":\"M. Valluri, R. Govindarajan\",\"doi\":\"10.1109/HIPC.1998.738006\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Modulo scheduling is an aggressive scheduling technique for loops that exploit instruction-level parallelism by overlapping successive iterations of the loop. Due to the nature of modulo scheduling, the lifetime of a variable can overlap with a subsequent definition of itself. To handle such overlapping lifetimes, modulo-variable expansion (MVE) is used, wherein the constructed schedule is unrolled a number of times. We propose a technique to improve the constructed schedule while performing MVE. In our approach, we unroll the data dependence graph of the original loop and re-schedule it with a MVE-sensitive scheduler. Such an approach is expected to result in better initiation rates as compared to the traditional approach. We have implemented our approach and evaluated its performance on a large number of scientific benchmark kernels.\",\"PeriodicalId\":175528,\"journal\":{\"name\":\"Proceedings. Fifth International Conference on High Performance Computing (Cat. No. 98EX238)\",\"volume\":\"87 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1998-12-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. Fifth International Conference on High Performance Computing (Cat. No. 98EX238)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HIPC.1998.738006\",\"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. Fifth International Conference on High Performance Computing (Cat. No. 98EX238)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HIPC.1998.738006","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modulo scheduling is an aggressive scheduling technique for loops that exploit instruction-level parallelism by overlapping successive iterations of the loop. Due to the nature of modulo scheduling, the lifetime of a variable can overlap with a subsequent definition of itself. To handle such overlapping lifetimes, modulo-variable expansion (MVE) is used, wherein the constructed schedule is unrolled a number of times. We propose a technique to improve the constructed schedule while performing MVE. In our approach, we unroll the data dependence graph of the original loop and re-schedule it with a MVE-sensitive scheduler. Such an approach is expected to result in better initiation rates as compared to the traditional approach. We have implemented our approach and evaluated its performance on a large number of scientific benchmark kernels.