CRI: IAD: A Service-Oriented Architecture for The Computation, Visualization, and Management of Scientific Data

现在，先进的计算能力对于解决科学和工程中的复杂问题至关重要。计算机模型已经成为基础研究的基础。它们在处理被建模的物理系统的微妙参数的现实细节方面越来越有用。在计算机模型日益增长的相关性的基础上，是可用的低成本计算能力的巨大增长，而且是相对最近的增长。计算能力的这些进步以及计算机建模和仿真带来了科学计算的新兴领域，在这个领域中，来自多个学科的科学家和工程师合作来理解仅仅几年前还无法接近的各种复杂系统。该基础设施项目提议在犹他大学科学计算与成像研究所（SCI研究所）建立一个基于高性能gpu的计算集群。与使用cpu进行计算和gpu进行图形和可视化任务的标准方法相反，本项目旨在探索gpu在一般计算中的能力。我们的目标是获得比当前基于cpu的计算集群所能实现的性能更高的每个节点的性能。每个节点有多个gpu，这种独特的计算资源使科学家能够有效地进行日益复杂的科学计算。提议的基础设施还包括用于可视化的高分辨率显示墙，以及数据库和Web服务器。该基础设施使研究能够有效执行模拟；简化复杂数据产品的创建；生成高质量的可视化；科学的工作流程简化了设计模拟和分析结果的过程。这个项目有可能影响各种各样的应用程序，在这些应用程序中，计算、可视化和科学数据管理目前是瓶颈。通过我们的跨学科合作，该项目将对帮助改进科学发现过程产生直接影响。研究生和本科生参与该项目将提供指导机会。将提供与项目目标紧密结合的新课程，包括使用gpu的并行编程课程和科学数据管理课程。私立学校致力于招收少数民族学生。该项目的结果将作为研究论文和免费工具在项目网站（http://www.cs.utah.edu/~csilva/projects/NSF-IIS-0751152/）上发布。

Advanced computing capabilities are now essential to solve complex problems in science and engineering. Computer models have become fundamental to basic research. They are increasingly useful in addressing, in realistic detail, the subtle parameters of the physical systems being modeled. Underlying this increasing relevance of computer models is the enormous, and relatively recent, increase in available low-cost compute power. These advances in computing power and hence in computer modeling and simulation have brought about the emergent field of scientific computing, in which scientists and engineers from multiple disciplines collaborate in understanding a variety of complex systems not approachable only a few years ago.This infrastructure project proposes the establishment of a high-performance GPU-based computing cluster at the Scientific Computing and Imaging Institute (SCI Institute) at the University of Utah. In contrast to the standard approach of using CPUs for computation and GPUs for graphics and visualization tasks, this project aims to explore the power of GPUs for general computations. Our goal is to obtain substantially higher performance per node than what can currently be accomplished with a CPU-based computing cluster. With multiple GPUs per node, this unique computational resource enables scientists to efficiently carry out increasingly complex scientific computations. The proposed infrastructure also includes a high-resolution display wall to be used for visualization, and database and Web servers.The infrastructure enables research in efficient execution of simulations; streamlining the creation of complex data products; producing high-quality visualizations; and scientific workflows leading to simplifying the process of designing simulations and analyzing their results. This project has the potential to impact a variety of applications where computation, visualization, and management of scientific data are currently the bottlenecks. Through our interdisciplinary collaborations, this project will have immediate impact in helping improve the scientific discovery process. The involvement of graduate and undergraduate students in the project will provide mentoring opportunities. New courses will be offered that are tightly integrated with the goals of the project, including courses on parallel programming using GPUs and on scientific data management. The PIs are committed to recruiting minority students.The results of this project will be disseminated as research papers and as freely available tools in the project website (http://www.cs.utah.edu/~csilva/projects/NSF-IIS-0751152/).