Harnessing Scalable Libraries for Statistical Computing on Modern Architectures and Bringing Statistics to Large Scale Computing

该项目旨在提高统计界对大中型平台上高性能计算的参与。理论统计学家可能对涉及大数据和HPC的科学做出很大贡献，但在大型平台上实现时，他们面临着低级编程语言，库和运行时环境，这些语言，库和运行时环境构成了足够高的障碍，阻止大多数人进入。该项目的核心是通过使用HPC社区的最先进方法来弥合大多数障碍，从而使该社区能够进行大规模的实验。这项研究的更广泛的影响包括为统计和数据科学界的HPC可扩展软件重用开辟了一条新的途径，从而为HPC软件研究提供了更多的、更面向数据的反馈。此外，HPC参与的统计社区可以将统计科学带入超级计算中的现代问题，这些问题越来越需要统计思维来量化不确定性。用于统计计算的开源R编程语言和环境是该项目的理想工具，因为它目前主导着统计学的新工作，并且在许多其他数据支持的科学社区中得到广泛使用和普及。该项目将在具有长期意义的接口上将R语言连接到高度可扩展的HPC库，并且在大多数情况下不需要改变当前的编程实践。此外，将在R内部开发易于使用的组件，以便直观地使用这些库在大型计算平台上进行大数据输入和数据操作，并桥接HPC运行时环境。将利用包括文件、实例、在一些重要会议上的教程时间表和讲习班在内的外联活动，将该项目的成果带给统计和其他数据支持的科学界。

This project aims to increase participation in high performance computing (HPC) on medium- to large-scale platforms by the statistics community. Theoretical statisticians potentially have strong contributions to science where big data and HPC are involved, yet in implementation on large platforms they face low-level programming languages, libraries, and runtime environments that pose a high enough barrier to prevent most from entering. This project is centered on enabling exactly this community to experiment at a large scale by bridging most of the barriers while using state-of-the-art approaches from the HPC community. Broader impacts of this research include opening a new avenue for HPC scalable software reuse by the statistics and the data science communities, thus providing additional and more data-oriented feedback to HPC software research. Further, an HPC-engaged statistics community can bring statistical science to modern issues in supercomputing that are increasingly in need of statistical thinking for quantifying uncertainty.The open source R programming language and environment for statistical computing is an ideal vehicle for the project as it currently dominates new work in statistics and it is widely used and rising in popularity in many other data-enabled science communities. This project will connect the R language to highly scalable HPC libraries at interfaces that make long-term sense and in a way that in most cases requires no change from current programming practice. In addition, ease-of-use components will be developed inside R for intuitive use of these libraries for big data input and data manipulation on large computing platforms and to bridge HPC runtime environments. Outreach consisting of documentation, examples, a schedule of tutorials at a number of key conferences, and workshops will be used to bring the results of this project to the statistics and other data-enabled science communities.