Elements: Sustained Innovation and Service by a GPU-accelerated Computation Tool for Applications of Topological Data Analysis

要素：GPU加速计算工具在拓扑数据分析应用中的持续创新和服务

• 批准号: 2310510
• 负责人: Xiaodong Zhang
• 金额: $ 60万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310510&HistoricalAwards=false
• 关键词: Elements; Sustained; Innovation; Service; GPU

Topological data analysis (TDA) and persistent homology (PH) are powerful algebraic topology-based methods for extracting significant topological features in large datasets, with diverse applications ranging from astronomy to biology, social sciences, and artificial intelligence (AI). However, with the rapid advancement of data instrumentation and generative data production, TDA tasks have become increasingly complex and computationally intensive. Thus, to keep up with the ever-evolving technological trends of hardware-accelerated software and increasing demand for fast topological data analytics, a highly efficient parallel TDA software is crucial. To address this need, the PIs of this project develop an open-source GPU-accelerated software tool for various TDA applications, in collaboration with domain experts, software developers, and high-performance computing researchers.The objective of this project is to enhance the computing efficiency and ensure sustained innovation and service of the GPU-accelerated computation tool. The specific goals of the project are fourfold: (1) to design and implement enhanced algorithms that efficiently parallelize computations and minimize data movement; (2) to enable our TDA software on a heterogeneous computing platform; (3) to design a graphical user interface that attracts non-GPU programming users and allows integration with other user-level TDA software tools; and (4) to test and evaluate our open-source software through state-of-the-art applications in AI and machine learning. These developments will provide timely hardware-acceleration solutions for TDA applications, make TDA more accessible to scientists and data analysts across disciplines, and advance scientific discoveries in the TDA community and industries.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.

拓扑数据分析（TDA）和持久性同源性（PH）是基于代数拓扑的强大方法，用于在大型数据集中提取重要的拓扑特征，其应用从天文学到生物学，社会科学和人工智能（AI）不等。但是，随着数据仪器和生成数据生产的快速发展，TDA任务变得越来越复杂和计算密集。因此，为了跟上硬件加速软件的不断发展的技术趋势，以及对快速拓扑数据分析的需求不断增长，高效的并行TDA软件至关重要。为了满足这一需求，该项目的PI与域专家，软件开发人员和高性能计算研究人员合作开发了用于各种TDA应用程序的开源GPU加速软件工具。该项目的目的是提高计算效率并确保持续的GPU-aCcelation Computation Computation Computation Computation Computation Computation Computation工具。该项目的具体目标是四倍：（1）设计和实施增强的算法，以有效地平行计算并最大程度地减少数据移动； （2）在异质计算平台上启用我们的TDA软件； （3）设计一个图形用户界面，该界面吸引非GPU编程用户并允许与其他用户级的TDA软件工具集成； （4）通过AI和机器学习中的最新应用程序测试和评估我们的开源软件。这些事态发展将为TDA应用提供及时的硬件加速解决方案，使TDA跨学科的科学家和数据分析师更容易获得TDA，并在TDA社区和行业中提高了科学发现。该奖项反映了NSF的法定任务，并通过该基金会的知识优点和广泛的criperia来评估NSF的法定任务。