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

该项目收购了一个96节点/1536核、Infiniband互连和10TB存储的皓龙集群，促进了计算机科学和生物学接口的丰富多样性的研究。这项将被启用的研究有许多应用，从亚分子到生物体，规模都很大。这项工作的动机是一个共同的愿望，即推动新的计算方法达到利用可用的计算资源(无论是在算法进步方面还是在解决最大问题方面)可以解决最重要的问题的极限。该项目代表了广泛的方法，从基于物理的模拟，到基因组学和蛋白质组学，再到生物统计学，再到联合实验/计算方法。能够实现的研究可以归类为四个领域：-大分子结构的模拟和建模，-序列和基因组/蛋白质数据集的分析，-超大型数据集的建模，以及-基础计算机科学。除了实现研究之外，该仪器是将Linux集群的低成本与共享存储机器的能力相结合的商品计算方面的进步。超级计算机问世，总拥有成本非常低。重点包括：-基于量子力学派生力场的分子动力学模拟：了解驱动蛋白质折叠的疏水效应并更接近准确模拟蛋白质折叠的目标-核糖体结构水的建模：了解细胞环境中的蛋白质结构和功能-使用基因组序列和实验数据整合遗传网络：基于共同的基因功能和进化进化将不同的信息适当地结合到一个单一的统一框架中-全基因组比对和进化约束的推断：在群体遗传学和进化理论的界面上预测群体遗传变异对有机体功能的影响-基于准确而易处理的人类运动模拟，神经肌肉骨骼系统的模型和动脉血流的模拟：已知的应用价值领域-流体动力学、固体力学、图形、分割、计算机视觉的算法发展：计算机科学中具有很强数学成分的领域，以及应用方面，如电影动画

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