MRI: Acquisition of Cyberinfrastructure for Computational Research (CCR)

提案号：CNS 08-21727PI(s): Mellor-Crummey, John Clementi, Cecilia；Luay等;Peter A. Nordlander；机构：Rice University Houston， TX 80309-0572标题：MRI/Acq。: Acq。建议：该项目，获取仪器并运行用于研究的计算集群（CCR），实现代码开发，中等规模计算，准备可扩展的代码以在国家规模的资源上执行，远程超级计算机结果的后处理和可视化，以及研究培训。项目范围从支持技术的开发，如并行系统的编程模型和性能分析工具，到计算科学和工程研究，包括复杂硬件和软件设计的自动验证技术，多个全基因组的数据挖掘，研究大规模生态动力学，通过模拟确定纳米光子结构的特性，模拟蛋白质动力学以研究灵活性和功能，模拟生物物理系统中的传输特性，理解生理系统和工程设计中的流体结构相互作用。可用于共享研究计算的CPU小时数的增加有助于支持计算研究的爆炸性增长。因此，可以在更高的规模上支持更紧密耦合的计算；集中式计算设施可以安排更高的利用率；一个单一的系统可以操作和管理（而不是单个研究小组的集群集合），并且可以有效地吸收国家资源。在本地运行这个中等规模的计算集群可以补充TeraGrid并加速研究。特别是，从本地系统到桌面的更高带宽和更低延迟更好地支持使用带有图形用户界面（例如，用于调试、性能和可伸缩性分析以及数据可视化）的交互式工具进行代码开发，同时准备在国家规模的资源上执行代码。事实上，计算是科学探究不可或缺的工具，是传统理论和实验方法的补充。更广泛的影响：CCR加强了科学和工程领域数百名学生和博士后的研究生研究和培训。它通过在集成高性能计算和计算问题解决的课程中使用，为本科生和研究生提供教育经验。此外，新兵训练营将使学生熟悉高性能计算并教授解决计算问题的技能。该工具还有助于研究生教育和教授联盟（AGEP）的招聘和教育工具，这是一个成功的项目，吸引了代表性不足的群体的成员进入高等教育，从而扩大了他们的教育经验。

Proposal #: CNS 08-21727PI(s): Mellor-Crummey, John Clementi, Cecilia; Nakhleh, Luay; Nordlander, Peter A.; Tezduyar, Tayfun E..Institution: Rice University Houston, TX 80309-0572Title: MRI/Acq.: Acq. of Cyberinfrastructure for Computational Research (CCR)Project Proposed:This project, acquiring instrumentation and operating a computational cluster for research (CCR), enables code development, medium scale computation, preparation of scalable codes for execution on national-scale resources, post-processing and visualization of results from remote supercomputers, and research training. Projects range from development of enabling technologies, such as programming models and performance analysis tools for parallel systems, to computational science and engineering research that includes techniques for automated verification of complex hardware and software designs, data mining of multiple whole genomes, studying large-scale ecological dynamics, determining the properties of nanophotonic structures through simulation, simulating protein dynamics to study flexibility and function, modeling transport properties in biophysical systems, and understanding fluid structure interactions in physiological systems and engineering designs. The increase in the number of CPU hours available for shared research computing contributes to support the explosive growth of computational research. Hence, more tightly coupled computing can be supported at a higher scale; a centralized computing facility can be scheduled for higher utilization; a single system can be operated and administered (rather than collections of cluster for individual research groups, and may effectively on-ramp national resources. Operating this modest-size computational cluster locally complements the TeraGrid and accelerates research. In particular, higher bandwidth and lower latency to the desktop from a local system better supports code development using interactive tools with graphical user interfaces (e.g., for debugging, performance, and scalability analysis, and data visualization) while preparing codes for execution on national-scale resources. Indeed, computation is an indispensable tool for scientific inquiry and complements traditional approaches of theory and experimentation.Broader Impacts:CCR enhances the graduate research and training of hundreds of students and post-doctoral fellows in science and engineering. It provides educational experience for undergraduate and graduate students through its use in courses that integrate high performance computing and computational problem solving. Furthermore, a boot-camp will familiarize students with high performance computing and teach computational problem solving skills. The instrument also contributes to recruiting and serves as an educational tool in the Alliance for the Graduate Education and the Professoriate (AGEP), a successful program in attracting members from underrepresented groups into higher education, thus broadening their educational experience.