MRI: Acquisition of a Hybrid GPU Computing Cluster High-End Applications in Science and Engineering

MRI：收购混合GPU计算集群 科学与工程高端应用

• 批准号: 1126709
• 负责人: Daniel Andresen
• 金额: $ 70万
• 依托单位: Kansas State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1126709&HistoricalAwards=false
• 关键词: MRI; Acquisition; Hybrid; GPU; Computing

Proposal #: CNS 11-26709PI(s): Andresen, Daniel; Caragea, Doina; Dodds, Walter K; Esry, Brett; Steward, David R. Institution: Kansas State UniversityTitle: MRI/ Acq.: A Hybrid GPU Computing Cluster High-End Applications in Science and EngineeringProject Proposed:This project, acquiring a hybrid computing cluster for high-end applications in science and engineering, services, among others, bioinformatics, ecological modeling, and physics. GPU-based computing support for distributed memory parallel applications constitutes a specific novel feature of the cluster.The work aims to support the following activities:- Modeling genomes, hyper-extractive economies, and ecological forecasting (by bringing much greater computational power) and,- Enabling new science utilizing the instrument to develop new algorithms in physics modeling and genomics. Broader Impacts:Mainly, the multidisciplinary nature of the proposed research provides the broader impacts. Expected are large broader impacts on basic physics research. It is expected to also impact on medicine through the development of better molecular models to view how our proteins and membranes interact. Planned are training activities of a new generation of researchers in tools and techniques for high-performance computing. The PIs would build on the broad past experience in preparing widely-used undergraduate and graduate educational materials, allowing students to perform real-world projects and impacting K-12 and STEM education. Hence, the project aims to significantly enhance and integrate our educational efforts at the K-12, undergraduate and graduate levels in bioinformatics.

提案＃：CNS 11-26709PI（S）：Andresen，Daniel； Caragea，Doina；多德斯，沃尔特·K；埃斯里（Esry），布雷特（Brett）； Steward，David R.机构：堪萨斯州立大学特学：MRI/ ACQ。：提出的混合GPU计算群集群集群高端应用高端应用：该项目，在科学和工程中获得高端应用，在科学和工程中获得混合计算群集，以及其他，包括生物信息，生物构成学，生态模型和物理学。 基于GPU对分布式内存并行应用程序的计算支持构成了集群的特定新颖特征。该工作旨在支持以下活动： - 建模基因组，超级提取性经济体以及生态预测（通过带来更大的计算能力），并 - 实现新的科学来利用新的科学来开发新的Algorith在物理学中和基因模型中。更广泛的影响：拟议研究的多学科性质主要是更广泛的影响。 预期是对基本物理研究的广泛影响。预计它也会通过开发更好的分子模型来影响医学，以查看我们的蛋白质和膜如何相互作用。计划的是新一代研究人员在高性能计算的工具和技术方面的培训活动。 PI将基于过去在准备广泛使用的本科和研究生教育材料的经验，使学生能够执行现实世界中的项目并影响K-12和STEM教育。因此，该项目旨在显着加强和整合我们在生物信息学的K-12，本科和研究生水平上的教育工作。