MRI-R2: Acquisition of an Applied Computational Instrument

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。在之前smri资助项目成功的基础上，俄勒冈大学一个由计算机科学家、心理学家、生物学家、化学家和物理学家组成的跨学科小组正在获得一个大规模的计算资源，即科学合成应用计算仪器（ACISS），以支持这些领域的前沿科学研究。ACISS硬件将包括通用多核计算节点、GPGPU加速增强的高性能计算节点、400TB存储系统、高带宽网络基础设施和额外的计算资源，这些资源将被纳入计算机和信息科学系现有的可视化实验室。提议的基础设施的一个关键部分是将ACISS作为计算科学云进行管理的独特机会。ACISS基础设施将允许扩展当前项目的范围，包括用于性能测量的软件工具、用于描述和执行复杂模拟和科学工作流程的编程环境和语言、用于多序列比对和系统发育推断的新算法，并承担支持领域科学的新项目。将受益的研究项目包括：a)建模秀丽隐杆线虫的神经网络，以更好地了解负责趋化性和趋化性的神经机制，以及研究参与发育的基因进化及其在物种形成和表型变异中的作用；b)开发用于脑成像和分析的神经信息学技术，将来自功能磁共振成像和其他来源的结构信息与脑电图数据相结合；C)分子模拟研究，包括中尺度模拟新技术的定义和应用计算方法来理解相变和固氮；D)影响行星形成早期阶段的湍流等离子体流的天体物理模拟。ACISS基础设施将为未来的多学科研究提供必要的计算资源。ACISS将为计算科学的研究和实践建立一个新的范例。在早期采用ACISS云计算技术中获得的经验将使我们能够更快地应用这些知识来创建新的科学工作流程，更富有成效的研究合作，并加强多学科教育计划。更进一步说，ACISS可以被看作是转化计算科学的一个模型，在这个模型中，基于ACISS的服务可以作为网络孵化器，为科学研究提供新的工作流程原型。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Building on the success of a previousMRI-funded project, an interdisciplinary group of computer scientists, psychologists, biologists, chemists, and physicists at the University of Oregon is acquiring a large-scale computational resource, the Applied Computational Instrument for Scientific Synthesis (ACISS), to support continued cutting-edge scientific research in these areas. The ACISS hardware will consist of general purpose multicore computing nodes, high performance computing nodes augmented with GPGPU acceleration, a 400TB storage system, high-bandwidth networking infrastructure and additional computing resources that will be incorporated into an existing visualization lab in the Department of Computer and Information Science. A key part of the proposed infrastructure is the unique opportunity to manage ACISS as a computational science cloud. The ACISS infrastructure will allow an expanded the scope for the current projects in the areas of software tools for performance measurement, programming environments and languages for describing and executing complex simulations and scientific work flows, new algorithms for multiple sequence alignment and phylogenetic inference and undertake new projects in support of the domain sciences. Research projects that will benefit include: a) modeling neural networks in C. elegans to better understand the neural mechanisms responsible for chemotaxis and klinotaxis, and investigation of the evolution of genes involved in development and their role in speciation and phenotypic variation; b) development of neuroinformatic techniques used in brain imaging and analysis, integrating structural information from fMRI and other sources with EEG data; c) molecular modeling research, including the definition of new techniques for meso-scale modeling and applying computational methods to understand phase transitions and nitrogen fixation; d) astrophysical simulations of turbulent plasma flows that influence the early stages of planet formation. The ACISS infrastructure will provide the computational resources necessary for future multidisciplinary research. ACISS will establish a novel paradigm for computational science research and practice. The experience gained in early adoption of the ACISS cloud computing technologies will allow us to more rapidly apply this knowledge to create new scientific work flows, more productive research collaborations, and enhanced multidisciplinary education programs. Farther reaching, ACISS can be seen as a model for translational computational science, in which ACISS-based services function as cyber-incubators where new work flows for scientific research are prototyped.