{"title":"Cone clustering principles for parallel logic simulation","authors":"K. Hering, R. Reilein, S. Trautmann","doi":"10.1109/MASCOT.2002.1167064","DOIUrl":null,"url":null,"abstract":"Parallelization following the replicated worker principle can significantly accelerate functional logic simulation of microprocessor structures. Successful application of this method strongly depends on circuit model partitioning. We have developed a hierarchical partitioning strategy with prepartitioning and main partitioning as core phases that appear as bottom-up cone clustering. Cones can be seen as special areas of combinational logic which have the ability to directly influence storing or output elements of a circuit model under consideration. We describe and compare three of our cone clustering techniques which are based on a formal model of parallel logic simulation. Experimental results are given with respect to IBM processor structures ranging in their size from several hundred thousand to several million basic elements at a mixture of register-transfer- and gate level.","PeriodicalId":384900,"journal":{"name":"Proceedings. 10th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunications Systems","volume":"259 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. 10th IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunications Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MASCOT.2002.1167064","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Parallelization following the replicated worker principle can significantly accelerate functional logic simulation of microprocessor structures. Successful application of this method strongly depends on circuit model partitioning. We have developed a hierarchical partitioning strategy with prepartitioning and main partitioning as core phases that appear as bottom-up cone clustering. Cones can be seen as special areas of combinational logic which have the ability to directly influence storing or output elements of a circuit model under consideration. We describe and compare three of our cone clustering techniques which are based on a formal model of parallel logic simulation. Experimental results are given with respect to IBM processor structures ranging in their size from several hundred thousand to several million basic elements at a mixture of register-transfer- and gate level.