II-New: System Acquisition for the Development of Scalable Parallel Algorithms for Scientific Computing

II-新：用于开发科学计算可扩展并行算法的系统获取

• 批准号: 0958512
• 负责人: Stephen Siegel
• 金额: $ 74.98万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-01 至 2013-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0958512&HistoricalAwards=false
• 关键词: II; New; System; Acquisition; Development

Parallel computing now plays a central role in nearly every science and engineering endeavor. The leading supercomputers today comprise hundreds of thousands of processors, and in the near future are expected to comprise millions of processors. Researchers at the University of Delaware are working to harness this vast computing power to make ground-breaking scientific discoveries. This work requires fundamental algorithmic research and development to address issues such as multiscale coupling, scalability, and hybrid message-passing/threaded programming.The investigators are building a new, highly parallel, computing system for scientific computation research and teaching at the University of Delaware. This computer consists of approximately 1,600 processors, and is expandable to many more. UD researchers who share a common interest in scalable parallel computation plan to explore a variety of scientific applications: 3D Navier-Stokes solvers for environmental turbulent flows; "ensemble analysis" in oceanic forecasting; computational electromagnetism; simulation of emulsion microstructure for environmental pollution control and material synthesis; electronic structure calculations for atoms and molecules; multiscale cloud dynamics for more reliable weather and climate prediction; computational carbon nanoelectronics; and vorticity-dominated flows. The new computer will be used to optimize the current algorithms in each application, help identify limitations to scalability, and explore new ideas to enable scaling to hundreds of thousands of processors and to take advantage of hybrid architectures. The system motivates collaboration among faculty interested in application problems and faculty with expertise in computer science. Libraries for efficient parallel data redistribution and automatic optimization of parallel codes are some of the ways the computer scientists participating in the project are enhancing the scientific applications.

现在，平行计算在几乎所有科学和工程努力中都起着核心作用。 今天的领先超级计算机包括数十万个处理器，预计在不久的将来将组成数百万个处理器。 特拉华大学的研究人员正在努力利用这种庞大的计算能力，以使开创性的科学发现。 这项工作需要基本的算法研究和开发，以解决诸如多尺度耦合，可扩展性和混合消息通讯/螺纹编程等问题。 该计算机由大约1,600个处理器组成，并且可以扩展到更多。 在可扩展的平行计算计划中具有共同兴趣的UD研究人员探索各种科学应用：3D Navier-Stokes Solvers用于环境动荡流；海洋预测中的“合奏分析”；计算电磁;模拟环境污染控制和材料合成的乳液微观结构；原子和分子的电子结构计算；多尺度的云动力学，以实现更可靠的天气和气候预测；计算碳纳米电子学；和以涡度为主的流量。 新计算机将用于优化每个应用程序中当前的算法，有助于识别可扩展性的局限性，并探索新的想法以使扩展到成千上万的处理器并利用混合体系结构。 该系统激发了对应用程序问题感兴趣和具有计算机科学专业知识的教师之间的合作。 有效的并行数据重新分布和平行代码的自动优化的库是参与项目的计算机科学家增强科学应用程序的某些方式。