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

今天的科学发现是由计算和数据密集型研究驱动的，这些研究利用了越来越多的可用数据。然而，新兴数据集的种类和大小往往使当前高性能计算（HPC）基础设施的分析具有挑战性，因为无法定制系统配置来高效地处理数据。该项目将获得和部署一个动态组合的计算机基础设施，称为肯塔基州研究信息组合云（KyRICC）。该KyRICC架构将支持复杂的数据分析管道，这些管道具有当前HPC基础设施目前不支持的高度异构的硬件要求。因此，该项目将促进和支持广泛的新研究活动。KyRICC将被数百名肯塔基州大学（英国）的研究人员（教师、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，这些机构包括中央学院、莫尔黑德州立大学、东肯塔基州大学、路易斯维尔大学、北方肯塔基州大学和肯塔基州州州立大学。作为一个领先的系统，KyRICC及其令人兴奋的项目将有助于招募学生，包括来自STEM中代表性不足的群体的学生，以进行计算研究。该系统还将加强对肯塔基州学院和大学的许多本科生、研究生和博士后的研究培训。KyRICC架构将支持复杂的数据分析管道，这些管道在各个数据分析步骤中具有高度异构的硬件要求。具体来说，KyRICC将集成四个子系统，以实现动态组合的云基础设施：（1）外围可组合的计算节点集群，允许单个节点上多达10个GPU和10 TB的主内存。节点组可以动态分配，以允许训练和推理非常大的深度学习模型和数据集;（2）下一代基于NVMe的高速存储集群，能够有效地为多GPU节点提供大量数据。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上对存储进行分区，使我们能够隔离数据并更好地管理系统性能;（3）由英国当前研究存储基础设施提供的Peta规模存储系统，提供总共2.2 PB的存储;以及（4）创新的工作量管理系统，用于动态基础设施组合、工作量分析、模型和基础设施调整，通过模板化项目支持通用管道以及机器和深度学习模型。KyRICC将成为一个区域计算资源，并将通过国家科学基金会支持的ACCESS项目提供给更广泛的国家计算研究基础设施。KyRICC支持的研究领域有望取得突破，包括深度学习和计算机视觉;自然语言处理和多模态嵌入;计算建模和模拟与数据分析;以及组学分析和系统集成。该项目由重大研究仪器（MRI）计划、刺激竞争研究的既定计划（EPSCoR）和计算机&信息科学&工程（CISE）共同资助。该奖项反映了NSF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。

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.