II-New: A Research Platform for Heterogeneous, Massively Parallel Computing

II-New：异构大规模并行计算研究平台

• 批准号: 1513126
• 负责人: Yicheng Tu
• 金额: $ 67.98万
• 依托单位: University of South Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1513126&HistoricalAwards=false
• 关键词: II; New; Research; Platform; Heterogeneous

The world of computing has entered the multi-core age. In addition to multi-core CPUs, co-processors containing thousands of computing cores in a single chip have become popular platforms for general-purpose computing. With the aggregated computing capabilities increasing at a steep rate, computing communities are still in an early stage in developing software systems, frameworks and applications to take full advantage of these new platforms. The co-existence of several different multi-core systems, including the Graphics Processing Units (GPUs), Intel?s Many Integrated Core (MIC) cards, and Accelerated Processing Units (APUs), further complicates the issue. This, on the other hand, provides opportunities for interesting research that spans different layers of the software stack. This infrastructure will support multiple, coordinated research projects that will develop frameworks and software systems enabling a new class of applications requiring high-performance computing capabilities.The main goal of this project is to build a computer cluster with heterogeneous, massive parallel computing capabilities to accelerate existing research and enable ground-breaking new research that shares the same need for intensive computation at the University of South Florida (USF). This project brings together eight USF investigators with research projects in several core disciplines of computer science and engineering: big data management, scientific computing, system security, hardware design, data mining, computer vision and pattern recognition. Specifically, the requested cluster supports research in: (1) design and optimization of a novel data stream management system architecture in a heterogeneous many-core hardware environment; (2) coarse-grained molecular simulation approach that allows accurate simulation of large-scale atomistic systems; (3) new system to deploy security policies that excel in both policy composition and runtime performance; (4) efficient modeling and design of energy-efficient and secure hardware systems; (5) automated interpretation of activities using pattern theory; (6) fast large scale clustering; and (7) pattern identification from biomedical image data.The intellectual merit of this project derives from the innovations of the individual projects and from the potential cross-disciplinary ideas it can germinate in the future. The infrastructure is expected to facilitate collaboration and cross-pollination of algorithms, models, representations, and data sets across individual project areas, building a collaborative network across the investigators. Furthermore, the cluster is expected to impact over a dozen application domains via on-going and planned research projects among the investigators and their collaborators throughout the USF system.Direct benefits to education and research will also be extended to the larger community through the applied aspects of projects, teaching and training. Project results and media content of the cluster will be showcased in the popular USF Engineering EXPO event, which seeks to educate and motivate K-12 students on math, science, engineering, and technology subjects.

计算世界已经进入了多核时代。除了多核CPU，在单个芯片中包含数千个计算核心的协处理器已经成为通用计算的流行平台。随着聚合计算能力的急剧增长，计算社区仍处于开发软件系统、框架和应用程序的早期阶段，以充分利用这些新平台。几种不同的多核系统共存，包括图形处理单元(GPU)、英特尔®S多集成核心(MIC)卡和加速处理单元(APU)，这进一步复杂化了问题。另一方面，这为跨越软件堆栈不同层的有趣研究提供了机会。该基础设施将支持多个协调的研究项目，这些项目将开发框架和软件系统，以支持需要高性能计算能力的新型应用程序。该项目的主要目标是建立一个具有异质、大规模并行计算能力的计算机集群，以加速现有研究，并使具有相同密集计算需求的突破性新研究能够在南佛罗里达大学(USF)实现。该项目汇集了8名USF调查人员，他们的研究项目涉及计算机科学和工程的几个核心学科：大数据管理、科学计算、系统安全、硬件设计、数据挖掘、计算机视觉和模式识别。具体地说，所请求的集群支持以下研究：(1)在异质多核硬件环境中设计和优化新型数据流管理系统体系结构；(2)允许精确模拟大规模原子系统的粗粒度分子模拟方法；(3)部署在策略组合和运行时性能上都出众的安全策略的新系统；(4)高效建模和设计节能且安全的硬件系统；(5)使用模式理论自动解释活动；(6)快速大规模集群；(7)从生物医学图像数据中进行模式识别。该项目的智力价值来自于单个项目的创新和未来可能萌发的潜在跨学科想法。预计该基础设施将促进跨单个项目领域的算法、模型、表示和数据集的协作和交叉授粉，从而在调查人员之间建立一个协作网络。此外，该集群预计将通过整个美国联邦调查人员及其合作者之间正在进行的和计划的研究项目影响十几个应用领域。教育和研究的直接好处也将通过项目、教学和培训的应用方面扩展到更大的社区。该集群的项目成果和媒体内容将在广受欢迎的美国联邦工程博览会活动中展示，该活动旨在教育和激励K-12学生在数学、科学、工程和技术科目上的学习。