High Performance and Visualization Cluster for Research in Coupled Computational Steering and Visualization for Large Scale Applications

用于大规模应用耦合计算引导和可视化研究的高性能和可视化集群

• 批准号: 0403313
• 负责人: Amitabh Varshney
• 金额: --
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0403313&HistoricalAwards=false
• 关键词: Performance; Visualization; Cluster; Research; Coupled

Researchers at the University of Maryland plan to build a high-performance computing and visualization cluster taking advantage of synergies afforded by coupling central processing units (CPUs), graphics processing units (GPUs), displays, and storage. The infrastructure will be used to support a broad program of computing research that will revolve around understanding, augmenting, and leveraging the power of heterogeneous vector computing enabled by GPU co-processors. The driving force here is the availability of cheap, powerful, and programmable graphics processing units (GPUs) through their commercialization in interactive 3D graphics applications, including interactive games. The CPU-GPU coupled cluster will enable the pursuit of several new research directions in computing, as well as enable a better understanding and fast solutions to several existing interdisciplinary problems through a visualization-assisted computational steering environment. In addition, it will foster research to cast several problems into a better spot on the price-performance curve.Intellectual Impact: The proposed research that will use this cluster falls into several broad interdisciplinary computing areas. The researchers plan to explore visualization of large datasets and algorithms for parallel rendering. In high-performance scientific computing we plan to develop and analyze efficient algorithms for use with complex systems when uncertainty is included in models. The researchers plan to use the cluster for several applications in computational biology, including computational modeling and visualization of proteins, conformational steering in protein structure prediction, folding, and drug design, large-scale phylogeny visualization, and sequence alignment.The researchers also plan to use the cluster for applications in real-time computer vision, real-time 3D virtual audio, and for efficient compilation of signal processing algorithms.Broader Impact: An important aspect of this research is to ensure a high impact of the cluster towards educational and outreach goals. The investigators plan to enrich their current coursework with research results obtained on the cluster. The coupled cluster with a large-area high-resolution display screen will serve as a valuable resource to present, interactively explore, evaluate, and validate the ongoing research in visualization, vision, scientific computing, human-computer interfaces, and computational biology with active participation of graduate as well as undergraduate students.

马里兰大学的研究人员计划利用中央处理单元（CPU），图形处理单元（GPU），显示器和存储提供的协同作用来建立高性能计算和可视化集群。基础架构将用于支持广泛的计算研究计划，该计划将围绕理解，增强和利用GPU协同处理器实现的异质矢量计算的力量。 这里的驱动力是通过交互式3D图形应用程序（包括交互式游戏）在包括交互式的3D图形应用程序中的商业化来提供便宜，功能强大且可编程的图形处理单元（GPU）。 CPU-GPU耦合群集将实现对计算中的几个新研究指导的追求，并通过可视化辅助的计算转向环境为几个现有的跨学科问题提供更好的理解和快速解决方案。此外，它将促进研究将几个问题置于价格绩效曲线上的一个更好的位置。智能影响：使用该集群的拟议研究属于几个广泛的跨学科计算区域。研究人员计划探索用于并行渲染的大型数据集和算法的可视化。在高性能科学计算中，我们计划在模型中包括不确定性时开发和分析与复杂系统一起使用的有效算法。研究人员计划将簇用于计算生物学的多种应用，包括蛋白质的计算建模和可视化，蛋白质结构预测，折叠和药物设计中的构象转向，大规模的系统发育的可视化以及序列的序列对齐。影响：这项研究的一个重要方面是确保集群对教育和外展目标产生重大影响。研究人员计划通过在集群上获得的研究结果来丰富他们当前的课程。带有大区域高分辨率显示屏的耦合群集将作为一种有价值的资源，可以介绍，交互探索，评估和验证正在进行的可视化，视觉，科学计算，人类计算机界面以及计算生物学以及研究生和小学生的积极参与的计算生物学研究。