Research Infrastructure: MRI: Acquisition of a GPU Cluster to Advance the Land Grant Mission at Washington State University Using AI-Driven Research

该项目将使华盛顿州立大学(WSU)能够获得一个名为CAMAS的高性能计算(HPC)集群，该集群集成了最先进的图形处理单元(GPU)加速器。GPU加速的计算机集群拥有更高密度的小型计算核心和矩阵处理器，将从根本上加快机器学习(ML)、人工智能(AI)、基因组学和模拟等计算密集型任务的速度。通过这种加速，CAMAS将在研究领域为科学计算提供新的能力，从使用深度学习的计算机硬件设计到植物基因组分析和气候建模。CAMAS将使GPU加速的研究集中在三个关键的科学和工程研究支柱上。这些支柱是使用ML的软件和硬件开发、自然系统的模拟和分析，以及用于基础设施管理、规划和决策的大数据分析。利用GPU加速以及AI和ML的应用范围从天体物理学和量子信息科学，到对人与自然系统的耦合建模和化学反应运输，再到优化电力市场需求响应的决策。CAMAS专题组将加强广泛的研究领域，促进跨学科知识的协同转移，并增加大学内部以及与区域和国家伙伴的合作机会。通过跨越WSU的三个主要校区和五个不同学院的广泛合作，CAMAS将在整个WSU系统中形成人工智能驱动的研究努力的核心，并为学生提供人工智能、ML和数据科学的培训机会。通过与学生领导的团体(包括Python和R工作组)以及本科生教育办公室的伙伴关系，CAMAS将增加学生对研究计算的参与，包括为代表不足的群体更多地参与STEM创造途径的战略活动。新的计算能力以及随之而来的整个研究界HPC计算素养的发展将进一步为教职员工和学生提供更多的区域伙伴关系和参与机会。CAMAS的项目网站和存储库将存放在https://hpc.wsu.edu/camas.上，其中包含到研究文物和项目活动细节的链接CAMAS项目网站将在华盛顿州立大学机构研究计算中心的主持下维护。这一奖项反映了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。

This project will enable Washington State University (WSU) to acquire a high-performance computing (HPC) cluster called Camas that incorporates state-of-the-art Graphics Processing Unit (GPU) accelerators. The cluster of GPU-accelerated computers, with their vastly greater density of small compute cores and matrix processors, will radically speed up computationally intensive tasks in machine-learning (ML), artificial intelligence (AI), genomics, and simulation. Through this speedup, Camas will provide new capabilities for scientific computing in research areas ranging from computer hardware design using deep learning to plant genome analysis and climate modeling.Camas will enable GPU-accelerated research that focuses on three key scientific and engineering research pillars. These pillars are software and hardware development using ML, simulation and analysis of natural systems, and big data analytics for infrastructure management, planning, and decision making. Applications that leverage GPU acceleration as well as AI and ML range from astrophysics and quantum information science, to modeling coupled human-natural systems and chemically-reactive transport, to decision making that optimizes demand response in electricity markets. The Camas cluster will strengthen a broad spectrum of research areas, promote the synergistic transfer of knowledge across disciplines, and increase collaborative opportunities both within the University as well as with regional and national partners. Through a breadth of collaboration that spans three of WSU's major campuses and five different Colleges, Camas will have broad impact in nucleating AI-driven research efforts throughout the WSU system as well as fostering training opportunities for students in AI, ML, and data science. Through partnerships with student-led groups including the Python and R Working Groups, as well as the Office of Undergraduate Education, Camas will increase student engagement in research computing including strategic activities that create a pathway for increasing participation of underrepresented groups in STEM. The new computational capabilities and the concomitant development of HPC computational literacy throughout the research community will further enable additional regional partnerships and engagement opportunities for both faculty and students. A project website and repository for Camas that holds links to research artifacts and details of project activities will be housed at https://hpc.wsu.edu/camas. The Camas project website will be maintained under the auspices of the Washington State University Center for Institutional Research Computing.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.