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）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，其小型计算核心和矩阵处理器的密度大大提高，将从根本上加快机器学习（ML）、人工智能（AI）、基因组学和模拟领域的计算密集型任务。通过这种加速，Camas将为研究领域的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。这些支柱是使用机器学习的软件和硬件开发，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加速以及人工智能和机器学习的应用范围从天体物理学和量子信息科学，到建模耦合的人类-自然系统和化学反应传输，再到优化电力市场需求响应的决策制定。Camas集群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合作伙伴的合作机会。通过横跨华盛顿州立大学三个主要校区和五个不同学院的广泛合作，Camas将在整个华盛顿州立大学系统中对人工智能驱动的研究工作产生广泛的影响，并为学生提供人工智能、机器学习和数据科学方面的培训机会。通过与学生领导的团体（包括Python和R工作组）以及本科教育办公室的合作，Camas将增加学生对研究计算的参与，包括为增加STEM中代表性不足群体的参与创造途径的战略活动。新的计算能力和伴随而来的HPC计算能力在整个研究界的发展，将进一步为教师和学生提供更多的区域合作伙伴关系和参与机会。Camas的项目网站和存储库包含了研究工件和项目活动细节的链接，将位于https://hpc.wsu.edu/camas。Camas项目网站将由华盛顿州立大学机构研究计算中心负责维护。该奖项反映了美国国家科学基金会的法定使命，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。

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.