Collaborative Research: Elements: ProDM: Developing A Unified Progressive Data Management Library for Exascale Computational Science

对极端尺度模拟和仪器产生的科学数据进行有效管理对于推进科学发现至关重要。由于数据的规模和科学分析的多样化需求，越来越需要以渐进的方式管理数据，这样用户就可以在减少数据移动和计算的情况下，尽可能多地传输数据来进行数据分析。然而，很少有人致力于创建强大的、可扩展的网络基础设施服务，将最新的进步方法中的算法创新与科学数据分析联系起来，使科学家无法获得这些功能。该项目旨在开发一个可持续的框架ProDM，支持科学数据的逐步管理，以促进其在科学应用中的使用。该项目的成功将通过提供一种管理和分析数据的新方法，促进新的科学研究和新的发现。此外，该项目的成果将以公开软件形式提供，以加强研究网络基础设施，促进教育和教学，并扩大对计算机的参与。ProDM的核心是统一可行的渐进表示和定制的原位和事后分析例程的开发。特别是，它涉及三个关键活动。首先，将建立一个数据引擎来统一最先进的渐进式表示，并为加速器提供便携式硬件支持，以及与其他数据管理和分析库的互操作软件接口。其次，将开发一个原位引擎，以促进使用渐进表示进行原位数据分析，其中包括重新设计原位语义和调整运行时动态。第三，将开发一个事后引擎，以有效地访问渐进式数据，并提高事后数据分析的数据检索性能。ProDM将部署在校园范围内的计算基础设施和领导系统中，用于与气候、聚变、分子动力学等现实世界的科学应用进行集成和评估。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。

Effective management of scientific data produced by extreme-scale simulations and instruments is crucial for advancing scientific discoveries. Due to the scale of data and the diverse requirements of scientific analytics, there is a growing need to manage data in a progressive manner, such that users can stream as much data as they need to carry out their data analytics with reduced data movement and computation. However, little effort has been put into creating robust and scalable cyberinfrastructure services that link the recent algorithmic innovations in progressive methods with scientific data analytics, leaving these capabilities inaccessible to scientists. This project aims to develop a sustainable framework ProDM that supports the progressive management of scientific data to facilitate its use in scientific applications. The success of this project will enable new scientific research and novel findings by providing a new way to manage and analyze data. Furthermore, outcomes of this project will be delivered as publicly available software to enhance research cyberinfrastructure, promote education and teaching, and broaden participation in computing. ProDM is centered upon the unification of viable progressive representations and tailored development for in-situ and post-hoc analytic routines. In particular, it involves three key activities. First, a data engine will be built to unify state-of-the-art progressive representations, and provide portable hardware support for accelerators as well as interoperative software interfaces to other data management and analytic libraries. Second, an in-situ engine will be developed to facilitate the use of progressive representations for in-situ data analytics, which include a redesign of in-situ semantics and adjustment of runtime dynamics. Third, a post-hoc engine will be developed to efficiently access progressive data and improve the performance of data retrieval for post-hoc data analytics. ProDM will be deployed on campus-wide computing infrastructures and leadership systems for integration and evaluation with real-world scientific applications from climate, fusion, molecular dynamics, and beyond.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.