{"title":"libyt: a Tool for Parallel In Situ Analysis with yt","authors":"Shin-Rong Tsai, Hsi-Yu Schive, Matthew Turk","doi":"10.25080/gerudo-f2bc6f59-011","DOIUrl":null,"url":null,"abstract":"—In the era of exascale computing, storage and analysis of large scale data have become more important and difficult. We present libyt , an open source C++ library, that allows researchers to analyze and visualize data using yt or other Python packages in parallel during simulation runtime. We describe the code method for organizing adaptive mesh refinement grid data structure and simulation data, handling data transition between Python and simulation with minimal memory overhead, and conducting analysis with no additional time penalty using Python C API and NumPy C API. We demonstrate how it solves the problem in astrophysical simulations and increases disk usage efficiency. Finally, we conclude it with discussions about libyt .","PeriodicalId":364654,"journal":{"name":"Proceedings of the Python in Science Conference","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Python in Science Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25080/gerudo-f2bc6f59-011","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
—In the era of exascale computing, storage and analysis of large scale data have become more important and difficult. We present libyt , an open source C++ library, that allows researchers to analyze and visualize data using yt or other Python packages in parallel during simulation runtime. We describe the code method for organizing adaptive mesh refinement grid data structure and simulation data, handling data transition between Python and simulation with minimal memory overhead, and conducting analysis with no additional time penalty using Python C API and NumPy C API. We demonstrate how it solves the problem in astrophysical simulations and increases disk usage efficiency. Finally, we conclude it with discussions about libyt .
在百亿亿次计算时代,大规模数据的存储和分析变得更加重要和困难。我们介绍了libt,一个开源的c++库,它允许研究人员在模拟运行时使用yt或其他Python包并行分析和可视化数据。我们描述了组织自适应网格细化网格数据结构和模拟数据的代码方法,以最小的内存开销处理Python和模拟之间的数据转换,并使用Python C API和NumPy C API进行分析而没有额外的时间损失。我们演示了它如何解决天体物理模拟中的问题并提高磁盘使用效率。最后,我们以对利比亚的讨论作为结束。