Elements:Software:NSCI: Empowering Data-driven Discovery with a Provenance Collection, Management, and Analysis Software Infrastructure

元素：软件：NSCI：通过来源收集、管理和分析软件基础设施支持数据驱动的发现

• 批准号: 1835892
• 负责人: Yong Chen
• 金额: $ 60万
• 依托单位: Texas Tech University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1835892&HistoricalAwards=false
• 关键词: Elements; Software; NSCI; Empowering; Data

Scientific breakthroughs are increasingly powered by advanced computing and data analysis capabilities delivered by high performance computing (HPC) systems. In the meantime, many scientific problems have moved to a level of complexity that the ability of understanding the results, auditing how a result is generated, and reproducing the important experiments or simulation results, is critical to scientists. Enabling such a capability in HPC systems requires a holistic collection, management, and analysis software infrastructure for "provenance" data, the metadata that describes the history of a piece of data. Such a software infrastructure does not exist yet, which motivates the proposed software development of a lightweight provenance service. With such a software element, many advanced data management functionalities such as identifying the data sources, parameters, or assumptions behind a given result, auditing data history and usage, or understanding the detailed process that how different input data are transformed into outputs can be possible. Responding to the National Strategic Computing Initiative, this project will provide an attractive software infrastructure to future national HPC systems to improve the productivity of science in complex HPC simulation and analysis cycles. The project team will also recruit underrepresented students, mentor graduate and undergraduate students, integrate results into curriculum, and publish and disseminate results.The lightweight provenance service software on HPC systems will provide: 1) an always-on, background service that automatically and transparently collects and manages provenance for scientific applications, 2) captures comprehensive provenance with accurate causality to support a wide range of use cases, and 3) provides easy-to-use analysis tools for scientists to quickly explore and utilize the provenance. This project will integrate the development, education, and outreach efforts tightly together.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.

科学突破越来越多地由高级计算（HPC）系统提供的高级计算和数据分析功能提供支持。同时，许多科学问题已经转移到了一种复杂程度，即了解结果的能力，审核结果的产生以及重现重要的实验或模拟结果，对科学家来说至关重要。在HPC系统中启用这种功能需要“出处”数据的整体收集，管理和分析软件基础架构，该数据是描述一份数据历史的元数据。这样的软件基础架构尚不存在，这激发了提议的轻巧出处服务的软件开发。有了这样的软件元素，许多高级数据管理功能，例如识别给定结果背后的数据源，参数或假设，审核数据历史记录和用法，或了解如何将不同的输入数据转换为输出的详细过程。为了响应国家战略计算计划，该项目将为未来的国家HPC系统提供一个有吸引力的软件基础架构，以提高复杂的HPC模拟和分析周期中科学的生产率。项目团队还将招募代表性不足的学生，导师毕业生和本科生，将结果整合到课程中，并发布和传播结果。收集和管理科学应用的出处，2）捕获具有准确因果关系的全面出处，以支持广泛的用例，3）提供易于使用的分析工具，供科学家快速探索和利用出处。该项目将紧密整合开发，教育和外展工作。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的审查标准来评估值得支持的。

项目成果

MonSTer: An Out-of-the-Box Monitoring Tool for High Performance Computing Systems

MonSTer：用于高性能计算系统的开箱即用的监控工具

• DOI: 10.1109/cluster49012.2020.00022
• 发表时间: 2020
• 期刊: IEEE International Conference on Cluster Computing (CLUSTER'20
• 作者: Li, Jie;Ali, Ghazanfar;Nguyen, Ngan;Hass, Jon;Sill, Alan;Dang, Tommy;Chen, Yang
• 通讯作者: Chen, Yang

MIQS: metadata indexing and querying service for self-describing file formats

• DOI: 10.1145/3295500.3356146
• 发表时间: 2019-11
• 期刊: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis
• 作者: Wei Zhang;S. Byna;Houjun Tang;Brody Williams;Yong Chen

Exploiting user activeness for data retention in HPC systems

利用用户活跃度来保留 HPC 系统中的数据

• DOI: 10.1145/3458817.3476201
• 发表时间: 2021
• 作者: Zhang, Wei;Byna, Suren;Sim, Hyogi;Lee, Sangkeun;Vazhkudai, Sudharshan;Chen, Yong
• 通讯作者: Chen, Yong

I/O characteristic discovery for storage system optimizations

• DOI: 10.1016/j.jpdc.2020.08.005
• 发表时间: 2021-02
• 期刊: J. Parallel Distributed Comput.
• 作者: Jiang Zhou;Yong Chen;Dong Dai;Zhuang Yu;Weiping Wang

RadarViewer : Visualizing the dynamics of multivariate data

RadarViewer：可视化多元数据的动态

• DOI: 10.1145/3311790.3404538
• 发表时间: 2020
• 期刊: Proceedings of Practice and Experience in Advanced Research Computing Conference Series (PEARC'20
• 作者: Nguyen, Ngan;Hass, Jon;Chen, Yong;Li, Jie;Sill, Alan;Dang, Tommy
• 通讯作者: Dang, Tommy