{"title":"PyTACC:得克萨斯高级计算中心的HPC Python","authors":"R. T. Evans, A. Gómez-Iglesias, W. C. Proctor","doi":"10.1145/2835857.2835861","DOIUrl":null,"url":null,"abstract":"Python-based applications at the Texas Advanced Computing Center (TACC) consume a significant and growing fraction of our computational resources. To meet this demand, TACC has developed an approach to provide its users a robust, performant, and flexible Python ecosystem. HPC Centers such as TACC have unique concerns when supporting Python due to their complex system environments and diverse user base: maintenance and usage tracking of multiple Python versions/distributions/compiler-bases and associated packages, deployment of this software in a manner that is compatible with our systems and readily usable to our users, optimization of the software to maximize scientific throughout, and user support and education.","PeriodicalId":171838,"journal":{"name":"Workshop on Python for High-Performance and Scientific Computing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"PyTACC: HPC Python at the texas advanced computing center\",\"authors\":\"R. T. Evans, A. Gómez-Iglesias, W. C. Proctor\",\"doi\":\"10.1145/2835857.2835861\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Python-based applications at the Texas Advanced Computing Center (TACC) consume a significant and growing fraction of our computational resources. To meet this demand, TACC has developed an approach to provide its users a robust, performant, and flexible Python ecosystem. HPC Centers such as TACC have unique concerns when supporting Python due to their complex system environments and diverse user base: maintenance and usage tracking of multiple Python versions/distributions/compiler-bases and associated packages, deployment of this software in a manner that is compatible with our systems and readily usable to our users, optimization of the software to maximize scientific throughout, and user support and education.\",\"PeriodicalId\":171838,\"journal\":{\"name\":\"Workshop on Python for High-Performance and Scientific Computing\",\"volume\":\"36 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Workshop on Python for High-Performance and Scientific Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2835857.2835861\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Workshop on Python for High-Performance and Scientific Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2835857.2835861","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
摘要
德克萨斯高级计算中心(Texas Advanced Computing Center, TACC)基于python的应用程序消耗了我们的计算资源中相当大且不断增长的一部分。为了满足这种需求,TACC开发了一种方法,为用户提供一个健壮、高性能和灵活的Python生态系统。由于其复杂的系统环境和多样化的用户基础,诸如TACC这样的高性能计算中心在支持Python时具有独特的关注点:维护和跟踪多个Python版本/发行版/编译器库和相关软件包的使用情况,以与我们的系统兼容并易于用户使用的方式部署该软件,优化软件以最大化科学贯穿,以及用户支持和教育。
PyTACC: HPC Python at the texas advanced computing center
Python-based applications at the Texas Advanced Computing Center (TACC) consume a significant and growing fraction of our computational resources. To meet this demand, TACC has developed an approach to provide its users a robust, performant, and flexible Python ecosystem. HPC Centers such as TACC have unique concerns when supporting Python due to their complex system environments and diverse user base: maintenance and usage tracking of multiple Python versions/distributions/compiler-bases and associated packages, deployment of this software in a manner that is compatible with our systems and readily usable to our users, optimization of the software to maximize scientific throughout, and user support and education.