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构建一个高性能的计算和可视化集群，利用中央处理器(CPU)、图形处理器(GPU)、显示器和存储的耦合所带来的协同效应。该基础设施将用于支持一个广泛的计算研究计划，该计划将围绕理解、增强和利用由GPU协处理器实现的异类向量计算的能力。这里的驱动力是廉价、强大和可编程的图形处理单元(GPU)的可用性，通过它们在交互式3D图形应用程序(包括交互式游戏)中的商业化。CPU-GPU耦合集群将使人们能够在计算领域追求几个新的研究方向，并通过可视化辅助的计算转向环境来更好地理解和快速解决几个现有的跨学科问题。此外，它还将促进研究，使几个问题在性价比曲线上表现得更好。智力影响：拟议中的使用该集群的研究属于几个广泛的跨学科计算领域。研究人员计划探索大数据集的可视化和并行渲染的算法。在高性能科学计算中，当模型中包含不确定性时，我们计划开发和分析用于复杂系统的高效算法。研究人员计划将该集群用于计算生物学中的几个应用，包括蛋白质的计算建模和可视化、蛋白质结构预测中的构象指导、折叠和药物设计、大规模的系统发育可视化和序列比对。研究人员还计划将该集群用于实时计算机视觉、实时3D虚拟音频和信号处理算法的高效编译。广泛影响：本研究的一个重要方面是确保该集群对教育和推广目标的高度影响。研究人员计划用在集群上获得的研究成果来丰富他们目前的课程。具有大面积高分辨率显示屏的耦合集群将成为展示、交互探索、评估和验证正在进行的可视化、视觉、科学计算、人机界面和计算生物学研究的宝贵资源，研究生和本科生都会积极参与。