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MRI Acquisition: Multi-Environment Research Computer for Exploration and Discovery (MERCED) Cluster

Abstract

Scientific computing plays a central role in knowledge discovery and scientific advancement. Over 13% of tenured/tenure-track faculty at the University of California Merced (UCM) specialize in areas focused on computational science. This expertise at UCM cuts through the university's three schools and includes faculty-led labs in the natural sciences, engineering, and social sciences. This proposal seeks support for the acquisition of a shared high-performance computing (HPC) cluster named the Multi-Environment Research Computer for Exploration and Discovery (MERCED). The cluster includes heterogeneous nodes, including nodes with large memory, nodes with large storage space, and GPU nodes. The three specific research areas benefit from this cluster, including modeling and simulation of complex systems, data enabled science, and numerical optimization. In the simulation of complex systems the cluster would advance research in high-performance molecular dynamics, computations of atomistic interactions for friction modeling, first principles simulations of chemical reactions and chemical biology, and fluid dynamics. In data enabled study, the cluster would impact the research in computational system biology, such as handling high-throughput genomics data, cognitive sciences, such as the evolution of language in human populations, history and world culture, such as integration of many datasets representing multiple spatial, temporal and thematic scales and permitting analysis and spatial visualization of this historical data in human affairs, and using GPU computing for speeding up the performance of database systems. In numerical optimization, the cluster would enhance the research in optimizing electric grid, GPU computing for 3D data processing, and signal reconstruction algorithms. The cluster enhances the educational and outreach activities at UCM as well as in the nearby universities.

People

Funding Source

Office of Advanced Cyberinfrastructure (OAC)

Project Period

2014-2017