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

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.