{"title":"自动格子数据生成","authors":"V. Ayyar, D. Hackett, W. Jay, E. Neil","doi":"10.1051/epjconf/201817509009","DOIUrl":null,"url":null,"abstract":"The process of generating ensembles of gauge configurations (and measuring various observables over them) can be tedious and error-prone when done \"by hand\". In practice, most of this procedure can be automated with the use of a workflow manager. We discuss how this automation can be accomplished using Taxi, a minimal Python-based workflow manager built for generating lattice data. We present a case study demonstrating this technology.","PeriodicalId":8440,"journal":{"name":"arXiv: High Energy Physics - Lattice","volume":"19 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2018-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Automated lattice data generation\",\"authors\":\"V. Ayyar, D. Hackett, W. Jay, E. Neil\",\"doi\":\"10.1051/epjconf/201817509009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The process of generating ensembles of gauge configurations (and measuring various observables over them) can be tedious and error-prone when done \\\"by hand\\\". In practice, most of this procedure can be automated with the use of a workflow manager. We discuss how this automation can be accomplished using Taxi, a minimal Python-based workflow manager built for generating lattice data. We present a case study demonstrating this technology.\",\"PeriodicalId\":8440,\"journal\":{\"name\":\"arXiv: High Energy Physics - Lattice\",\"volume\":\"19 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-02-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv: High Energy Physics - Lattice\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1051/epjconf/201817509009\",\"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: High Energy Physics - Lattice","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1051/epjconf/201817509009","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The process of generating ensembles of gauge configurations (and measuring various observables over them) can be tedious and error-prone when done "by hand". In practice, most of this procedure can be automated with the use of a workflow manager. We discuss how this automation can be accomplished using Taxi, a minimal Python-based workflow manager built for generating lattice data. We present a case study demonstrating this technology.
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