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CAREER: Advanced Containers for Reproducibility in Computational and Data Science

职业：计算和数据科学中可重复性的先进容器

基本信息

批准号：
1846418
负责人：
Tanu Malik
金额：
$ 49.89万
依托单位：
DePaul University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1846418&HistoricalAwards=false
关键词：
CAREER Advanced Containers Reproducibility Computational

项目摘要

Reproducibility is essential for scientific progress and to establish trust in scientific results. Published computational results, increasingly, lack sufficient capture and description of companion information that enables subsequent confirmation and extension of results. This project will design and implement a novel container-based approach for sharing and reproducing scientific results. Reproducible containers developed from this project will package code, data, environment, provenance, and assumptions across heterogeneous computing platforms. In contrast to taking a "devops"-based approach, which burdens the user to manage reproducibility of experiments, this project uses reference executions of scientific experiments as a virtualization method for containerizing associated artifacts. While a container-based approach can help to verify repeat computations, further advancements in container technology are needed to enable advanced forms of reproducibility. This project aims to enable reproducibility even if computations include non-determinism and race conditions; code, data-sets, and parameters are changed; computations are performed on distributed platforms; and containers are shared with sensitive data and undocumented content. To that end, the project will develop an open-source container runtime that will offer primitives for enabling re-runnability, extensibility, and publish-ability of containers. The work leverages portable containers developed previously for computational sciences. This award will lay the foundation for an essential building block for establishing reproducibility of real-world computational and data science use cases. The project will increase awareness of the need for computational reproducibility tools through an integrated research and education plan involving scientists, students, and instructors.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

可重复性对科学进步和建立对科学成果的信任至关重要。已发表的计算结果越来越缺乏对伴随信息的充分捕获和描述，从而无法对结果进行后续确认和扩展。该项目将设计和实施一种新的基于容器的方法来共享和复制科学成果。从该项目开发的可复制容器将跨异构计算平台打包代码、数据、环境、来源和假设。与采用“devops”为基础的方法（这会增加用户管理实验再现性的负担）相比，该项目使用科学实验的参考执行作为容器化相关工件的虚拟化方法。虽然基于容器的方法可以帮助验证重复计算，但容器技术的进一步发展需要实现高级形式的可再现性。这个项目的目标是即使计算包括非确定性和竞争条件也能实现再现性；代码、数据集和参数都改变了；计算在分布式平台上进行；容器与敏感数据和未记录的内容共享。为此，该项目将开发一个开源容器运行时，该运行时将提供支持容器的可重新运行性、可扩展性和可发布性的原语。这项工作利用了以前为计算科学开发的便携式容器。该奖项将为建立真实世界计算和数据科学用例的可再现性奠定基础。该项目将通过一项涉及科学家、学生和教师的综合研究和教育计划，提高人们对计算再现性工具需求的认识。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。