MRI: Acquisition of a Hybrid Shared-Memory / Massively-Parallel Commodity Cluster for Cost-Effective Super-Computing at Stanford

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This project, acquiring a 96-node/1536-core Opteron cluster with Infiniband interconnect and 10TB storage, facilitates a rich diversity of research at the interface of computer science and biology. The research to be enabled has many applications with a remarkable range of scale, from the sub-molecular to the organismal. The work is motivated by a common desire to push novel computational approaches to the limit that most significant problems can be tackled with available computational resources (both in terms of algorithmic advances and in terms of solving the largest). The project represents a broad range of methods, from physics-based simulation, to genomics and proteomics, to biostatistics, to joint experimental/ computational methodology. The enabled research can be grouped in four areas:-Simulation and modeling of macromolecular structures,-Analysis of sequence and genomic/proteomic datasets,-Modeling of very large datasets, and -Fundamental computer science.Besides enabling research, the instrument is an advance in commodity computing combining the low cost of Linux clusters with the power of shared memory machines. Out comes a supercomputer with a very low total cost of ownership. Highlights include:-Molecular dynamics simulation based on quantum mechanically derived force fields: to understand hydrophobic effect that drives protein folding and to get closer to the goal of accurately modeling protein folding-Modeling of structure water in ribosome: to understand protein structure and function in the cellular milieu-Integration of genetic networks using genome sequence and experimental data: to appropriately combine disparate information into a single unifying framework based on common gene function and evolutionary descent-Whole genomic alignments and inference of evolutionary constraints: to predict impact of population genetic variation on the function of the organism, at the interface of population genetics and evolutionary theory-Simulation of human motion based on accurate, yet tractable, models of the neuromusculoskeletal system and simulation of blood flow in aorta: areas known for applied value-Development of algorithms for fluid dynamics, solid mechanics, graphics, segmentation, computer vision: areas of computer science with a strong mathematical component, as well as applied aspects such as movie animations