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)提供资金的。在之前一个由核磁共振资助的项目的成功基础上，俄勒冈大学的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大规模计算资源--科学合成应用计算仪器(ACISS)，以支持这些领域的持续尖端科学研究。ACISS硬件将包括通用多核计算节点、增强了GPGPU加速的高性能计算节点、400TB存储系统、高带宽网络基础设施和其他计算资源，这些资源将并入计算机和信息科学部现有的可视化实验室。拟议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。该中心的基础设施将扩大现有项目的范围，这些领域包括：绩效衡量软件工具、描述和执行复杂模拟和科学工作流程的编程环境和语言、多序列比对和系统发育推断的新算法，以及开展支持领域科学的新项目。将受益的研究项目包括：a)对线虫的神经网络进行建模，以更好地了解趋化性和趋趋性的神经机制，并调查参与发育的基因的演变及其在物种形成和表型变化中的作用；b)开发用于大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数据结合起来；c)分子建模研究，包括新的中尺度建模技术的定义和应用计算方法来了解相变和固氮；d)对影响行星形成早期阶段的湍流等离子体流进行天体物理模拟。ACISS的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、更有成效的研究协作和增强的多学科教育计划。在更远的范围内，ACISS可以被视为转换计算科学的模型，在该模型中，基于ACISS的服务起到网络孵化器的作用，在这里，科学研究的新工作流程是原型。

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.