{"title":"GPaR: A Parallel Graph Rewriting Tool","authors":"S. Despréaux, A. Maignan","doi":"10.1109/SYNASC.2018.00021","DOIUrl":null,"url":null,"abstract":"GPaR is a parallel graph rewriting software implemented in C++ with a graphical user interface. Considering an initial graph g and a system of rewriting rules R = {li->ri, i = 1...n}, GPaR rewrites the graph g into a graph g' by using, simultaneously, the rules of R whose left-hand sides, li, match subgraphs of g. GPaR tackles the problem of overlapping matches and thus can be used in a large variety of rewriting problems including fractal systems. Our proposition is illustrated on the examples of adaptive mesh and Pythagorean tree. The performance of GPaR is compared to the performance of other tools on the Sierpinski triangle benchmark.","PeriodicalId":273805,"journal":{"name":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"26 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SYNASC.2018.00021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
GPaR is a parallel graph rewriting software implemented in C++ with a graphical user interface. Considering an initial graph g and a system of rewriting rules R = {li->ri, i = 1...n}, GPaR rewrites the graph g into a graph g' by using, simultaneously, the rules of R whose left-hand sides, li, match subgraphs of g. GPaR tackles the problem of overlapping matches and thus can be used in a large variety of rewriting problems including fractal systems. Our proposition is illustrated on the examples of adaptive mesh and Pythagorean tree. The performance of GPaR is compared to the performance of other tools on the Sierpinski triangle benchmark.