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

该项目获得了一个96节点/1536核的Opteron集群，具有Infiniband互连和10TB存储，促进了计算机科学和生物学界面的丰富多样性研究。从亚分子到有机体，这项研究的应用范围非常广泛。这项工作的动机是一种共同的愿望，即将新颖的计算方法推向极限，使最重要的问题可以用可用的计算资源来解决（无论是在算法进步方面还是在解决最大问题方面）。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，到生物统计学，再到联合实验/计算方法。所支持的研究可以分为四个领域：-大分子结构的模拟和建模，-序列和基因组/蛋白质组学数据集的分析，-超大型数据集的建模，以及-基础计算机科学。除了支持研究之外，该仪器还将Linux集群的低成本与共享内存机器的强大功能结合在一起，是商品计算的一大进步。超级计算机的总拥有成本非常低。重点包括：-基于量子力学推导力场的分子动力学模拟：了解驱动蛋白质折叠的疏水效应，更接近精确建模蛋白质折叠的目标-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-利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谱系，将不同的信息适当地结合到一个统一的框架中——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生物体功能的影响——基于精确而易于处理的神经肌肉骨骼系统模型和主动脉血流模拟的人体运动模拟；以应用价值而闻名的领域——流体动力学、固体力学、图形学、分割、计算机视觉算法的开发：具有强大数学成分的计算机科学领域，以及电影动画等应用方面

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