MRI: Acquisition of the Kentucky Research Informatics Composable Cloud (KyRICC)

MRI：收购肯塔基州研究信息学可组合云 (KyRICC)

• 批准号: 2216140
• 负责人: Jeffery Talbert
• 金额: $ 113.66万
• 依托单位: University of Kentucky Research Foundation
• 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: 2022-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2216140&HistoricalAwards=false
• 关键词: MRI; Acquisition; Kentucky; Research; Informatics

Scientific discovery today is driven by computation and data-intensive research that exploits the growing amounts of available data. However, the wide variety and size of emerging datasets often make analysis challenging on current high-performance computing (HPC) infrastructures because system configurations cannot be customized to process the data efficiently. This project will acquire and deploy a dynamically composable computer infrastructure called the Kentucky Research Informatics Composable Cloud (KyRICC). This KyRICC architecture will support complex data analysis pipelines with highly heterogeneous hardware requirements not currently supported by current HPC infrastructures. As a result, this project will enable and support a wide range of new research activities. KyRICC will be used by hundreds of University of Kentucky (UK) researchers (faculty, staff, and students) and by other computational research collaborators at institutions across the region including Centre College, Morehead State University, Eastern Kentucky University, University of Louisville, Northern Kentucky University, and Kentucky State University. As a leading-edge system, KyRICC and the exciting projects it makes possible will help recruit students, including students from groups underrepresented in STEM, to computational research. The system will also enhance the research training of many undergraduates, graduate students, and postdocs in Kentucky colleges and universities.The KyRICC architecture will support complex data analysis pipelines with highly heterogeneous hardware requirements across individual data analysis steps. Specifically, KyRICC will integrate four subsystems that will enable dynamically composable cloud infrastructure: (1) A cluster of peripheral-composable compute nodes, allowing for up to 10’s of GPUs and 10’s of TB of main memory on a single node. Groups of nodes can be dynamically allocated to allow the training and inference of very large deep learning models and datasets; (2) A next-generation high-speed NVMe-based storage cluster capable of efficiently serving large volumes of data to multi-GPU nodes. Unlike traditional clustered storage systems, this composable filesystem allows the partitioning of storage on the project-level, allowing us to isolate data and better manage system performance; (3) A Peta-scale storage system provided by UK’s current research storage infrastructure, providing a total of 2.2 PB of storage; and (4) An innovative workload management system for dynamic infrastructure composition, workload profiling, model and infrastructure tuning, supporting common pipelines and machine and deep learning models through templated projects. KyRICC will be a regional computational resource and will also be made available to the broader national computational research infrastructure through the NSF-supported ACCESS projects. Areas of expected breakthroughs in KyRICC-enabled research include deep learning and computer vision; natural language processing and multimodal embedding; computational modeling and simulation with data analytics; and omics analysis and systemic integration.This project is jointly funded by the Major Research Instrumentation (MRI) program, the Established Program to Stimulate Competitive Research (EPSCoR), and the Computer & Information Science & Engineering (CISE) Directorate.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.

今天的科学发现是由计算和数据密集型研究推动的，这些研究利用了越来越多的可用数据。然而，新出现的数据集种类繁多且规模庞大，这往往使分析在当前的高性能计算(HPC)基础设施上具有挑战性，因为无法定制系统配置来高效地处理数据。该项目将获得并部署一个动态可组合的计算机基础设施，称为肯塔基研究信息学可组合云(KyRICC)。这种KyRICC架构将支持具有高度异构性硬件要求的复杂数据分析管道，目前的HPC基础设施不支持这种要求。因此，该项目将支持和支持一系列新的研究活动。KyRICC将由数百名肯塔基大学(英国)的研究人员(教职员工和学生)以及该地区各机构的其他计算研究合作者使用，包括中心学院、莫尔黑德州立大学、东肯塔基大学、路易斯维尔大学、北肯塔基大学和肯塔基州立大学。作为一种前沿系统，KyRICC及其实现的激动人心的项目将有助于招收学生，包括来自STEM中代表性不足的群体的学生，从事计算研究。该系统还将加强肯塔基州许多本科生、研究生和博士后的研究培训。KyRICC架构将支持跨单个数据分析步骤具有高度异构性硬件要求的复杂数据分析管道。具体地说，KyRICC将集成四个子系统，以支持动态组合的云基础设施：(1)外围设备可组合的计算节点集群，允许在单个节点上支持高达10‘S的GPU和10’TB的S主存。可以动态分配节点组，以允许对超大型深度学习模型和数据集进行训练和推理；(2)基于NVMe的下一代高速存储集群，能够高效地向多个GPU节点提供海量数据。与传统的集群存储系统不同，这种可组合的文件系统允许在项目级别对存储进行分区，使我们能够隔离数据并更好地管理系统性能；(3)由英国当前研究的存储基础架构提供的Peta规模的存储系统，提供总计2.2 PB的存储；以及(4)用于动态基础架构组合、工作负载分析、模型和基础架构调优的创新工作负载管理系统，通过模板化项目支持通用管道和机器和深度学习模型。KyRICC将是一个地区性的计算资源，也将通过NSF支持的ACCESS项目提供给更广泛的国家计算研究基础设施。KYRICC支持的研究的预期突破领域包括深度学习和计算机视觉；自然语言处理和多模式嵌入；数据分析的计算建模和模拟；组学分析和系统集成。该项目由重大研究工具(MRI)计划、既定的激励竞争研究计划(EPSCoR)和计算机与信息科学与工程(CESE)理事会联合资助。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。