CC* Planning: High-Performance GPU Cluster for Computational Intensive Interdisciplinary Research

CC* Planning：用于计算密集型跨学科研究的高性能 GPU 集群

• 批准号: 2201592
• 负责人: Ahmet Arac
• 金额: $ 10万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2201592&HistoricalAwards=false
• 关键词: CC; Planning; Performance; GPU; Cluster

With recent technical advances in research methodologies, there is an ever-increasing generation of large datasets in biomedical research. This coincides with advances in analytical methodologies such as deep learning techniques. These analytical tools heavily utilize the power of graphical processing units (GPUs) as the main computational hardware. However, the current infrastructure of many research institutes is not sufficient to provide solutions for this kind of special computational needs of the biomedical fields. This results in attempts by individual research laboratories to solve these computational needs temporarily, slowing down the research and putting the difficult-to-obtain data at risk of loss. To address these issues and find a more efficient and sustainable solution, this project aims to systematically go through several steps at different levels, create solutions that will expedite the biomedical research and provide an institutional example for setting up infrastructure to meet these computational needs for biomedical research institutes. More specifically, the goal is to create a detailed plan of such an infrastructure. These include: (1) identification of science use cases and representative users, (2) determining the standards for computing, networking, storage and security of such a system, (3) creating a high-performance GPU architecture plan and (4) developing a support model to maintain and improve this infrastructure. This project aims to create a model that is efficient, useful, sustainable and that will expedite the data processing and analytics in biomedical research.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.

随着研究方法的最新技术进步，生物医学研究中出现了越来越多的大数据集。这与深度学习技术等分析方法的进步不谋而合。这些分析工具大量利用图形处理单元(GPU)作为主要计算硬件的能力。然而，目前许多研究机构的基础设施不足以为生物医学领域的这种特殊计算需求提供解决方案。这导致各个研究实验室试图暂时解决这些计算需求，减缓了研究速度，并使难以获得的数据面临丢失的风险。为了解决这些问题并找到更有效和更可持续的解决方案，该项目旨在系统地在不同层面上经历几个步骤，创建将加快生物医学研究的解决方案，并为建立基础设施以满足生物医学研究机构的这些计算需求提供一个机构范例。更具体地说，目标是为这样的基础设施制定一个详细的计划。这些措施包括：(1)确定科学用例和代表性用户；(2)确定此类系统的计算、联网、存储和安全标准；(3)创建高性能GPU架构计划；(4)开发支持模型，以维护和改进这一基础设施。该项目旨在创建一个高效、有用、可持续的模型，并将加快生物医学研究中的数据处理和分析。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。