{"title":"共享输入同时预积累的局部相邻关系","authors":"Johannes Blühdorn, Nicolas R. Gauger","doi":"arxiv-2405.07819","DOIUrl":null,"url":null,"abstract":"In shared-memory parallel automatic differentiation, shared inputs among\nsimultaneous thread-local preaccumulations lead to data races if Jacobians are\naccumulated with a single, shared vector of adjoint variables. In this work, we\ndiscuss the benefits and tradeoffs of re-enabling such preaccumulations by a\ntransition to suitable local adjoint variables. In particular, we assess the\nperformance of mapped local adjoints in discrete adjoint computations in the\nmultiphysics simulation suite SU2.","PeriodicalId":501256,"journal":{"name":"arXiv - CS - Mathematical Software","volume":"12 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Local Adjoints for Simultaneous Preaccumulations with Shared Inputs\",\"authors\":\"Johannes Blühdorn, Nicolas R. Gauger\",\"doi\":\"arxiv-2405.07819\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In shared-memory parallel automatic differentiation, shared inputs among\\nsimultaneous thread-local preaccumulations lead to data races if Jacobians are\\naccumulated with a single, shared vector of adjoint variables. In this work, we\\ndiscuss the benefits and tradeoffs of re-enabling such preaccumulations by a\\ntransition to suitable local adjoint variables. In particular, we assess the\\nperformance of mapped local adjoints in discrete adjoint computations in the\\nmultiphysics simulation suite SU2.\",\"PeriodicalId\":501256,\"journal\":{\"name\":\"arXiv - CS - Mathematical Software\",\"volume\":\"12 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Mathematical Software\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2405.07819\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Mathematical Software","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2405.07819","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Local Adjoints for Simultaneous Preaccumulations with Shared Inputs
In shared-memory parallel automatic differentiation, shared inputs among
simultaneous thread-local preaccumulations lead to data races if Jacobians are
accumulated with a single, shared vector of adjoint variables. In this work, we
discuss the benefits and tradeoffs of re-enabling such preaccumulations by a
transition to suitable local adjoint variables. In particular, we assess the
performance of mapped local adjoints in discrete adjoint computations in the
multiphysics simulation suite SU2.