Saibal De, Eduardo Corona, P. Jayakumar, S. Veerapaneni
{"title":"Scalable Solvers for Cone Complementarity Problems in Frictional Multibody Dynamics","authors":"Saibal De, Eduardo Corona, P. Jayakumar, S. Veerapaneni","doi":"10.1109/HPEC.2019.8916234","DOIUrl":null,"url":null,"abstract":"We present an efficient, hybrid MPI/OpenMP framework for the cone complementarity formulation of large-scale rigid body dynamics problems with frictional contact. Data is partitioned among MPI processes using a Morton encoding in order to promote data locality and minimize communication. We parallelize the state-of-the-art first and second-order solvers for the resulting cone complementarity optimization problems. Our approach is highly scalable, enabling the solution of dense, large-scale multibody problems; a sedimentation simulation involving 256 million particles ($\\sim 324$ million contacts on average) was resolved using 512 cores in less than half-hour per time-step.","PeriodicalId":184253,"journal":{"name":"2019 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE High Performance Extreme Computing Conference (HPEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HPEC.2019.8916234","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
We present an efficient, hybrid MPI/OpenMP framework for the cone complementarity formulation of large-scale rigid body dynamics problems with frictional contact. Data is partitioned among MPI processes using a Morton encoding in order to promote data locality and minimize communication. We parallelize the state-of-the-art first and second-order solvers for the resulting cone complementarity optimization problems. Our approach is highly scalable, enabling the solution of dense, large-scale multibody problems; a sedimentation simulation involving 256 million particles ($\sim 324$ million contacts on average) was resolved using 512 cores in less than half-hour per time-step.