Kazuhiro Kinkai, T. Baba, Hiroyoshi Jutori, K. Ootsu, Takeshi Ohkawa, T. Yokota
{"title":"Comparative Study of Path Prediction Method for Speculative Loop Execution","authors":"Kazuhiro Kinkai, T. Baba, Hiroyoshi Jutori, K. Ootsu, Takeshi Ohkawa, T. Yokota","doi":"10.1109/ICNC.2012.52","DOIUrl":null,"url":null,"abstract":"Execution path ratio is mostly dominated by two execution paths in program loops. We have developed Two-Path Limited Speculation Method that achieves speed-up in programs using optimization of the most frequent two paths and speculative multi-thread execution of them. The path predictor used in the method predicts the next execution path in Two-Path Limited Speculation Method, and plays an important role in performance improvement of the method. In this paper, we apply several well-known branch prediction methods to the path prediction and evaluate them in terms of mis-prediction ratio. Experimental results show that the mis-prediction ratios of the path predictors vary from 10% to 45%, depending on the benchmark programs, and are 20% on average, the Gshare path predictor performs best in eight path prediction methods.","PeriodicalId":442973,"journal":{"name":"2012 Third International Conference on Networking and Computing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Third International Conference on Networking and Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2012.52","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Execution path ratio is mostly dominated by two execution paths in program loops. We have developed Two-Path Limited Speculation Method that achieves speed-up in programs using optimization of the most frequent two paths and speculative multi-thread execution of them. The path predictor used in the method predicts the next execution path in Two-Path Limited Speculation Method, and plays an important role in performance improvement of the method. In this paper, we apply several well-known branch prediction methods to the path prediction and evaluate them in terms of mis-prediction ratio. Experimental results show that the mis-prediction ratios of the path predictors vary from 10% to 45%, depending on the benchmark programs, and are 20% on average, the Gshare path predictor performs best in eight path prediction methods.