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社区在大中型平台上对高性能计算（HPC）的参与。理论统计学家在涉及大数据和高性能计算的科学领域有很大的潜力，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，这些都构成了一个足够高的障碍，阻止大多数人进入。这个项目的核心是通过使用高性能计算社区最先进的方法，消除大多数障碍，使这个社区能够进行大规模的实验。这项研究的广泛影响包括为统计学和数据科学社区的HPC可扩展软件重用开辟了一条新的途径，从而为HPC软件研究提供了额外的、更多的面向数据的反馈。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载体，因为它目前在统计新工作中占主导地位，并且在许多其他支持数据的科学社区中被广泛使用并越来越受欢迎。这个项目将把R语言与高度可扩展的高性能计算库连接起来，这些接口具有长期意义，而且在大多数情况下，不需要改变当前的编程实践。此外，R将开发易于使用的组件，以便在大型计算平台上直观地使用这些库进行大数据输入和数据操作，并架起HPC运行时环境的桥梁。外联工作包括文件、实例、若干重要会议的教程时间表和讲习班，将用于将该项目的成果介绍给统计学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.