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

计算机世界已经进入了多核时代。除了多核cpu之外，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快速增长，计算社区在开发软件系统、框架和应用程序以充分利用这些新平台方面仍处于早期阶段。几个不同的多核系统共存，包括图形处理单元（gpu），英特尔？许多集成核心（MIC）卡和加速处理单元（apu）使问题进一步复杂化。另一方面，这为跨软件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，使需要高性能计算能力的新型应用程序成为可能。该项目的主要目标是建立一个具有异构、大规模并行计算能力的计算机集群，以加速现有的研究，并实现突破性的新研究，这些研究与南佛罗里达大学（USF）对密集计算的需求相同。该项目汇集了8名南加州大学的研究人员，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。具体而言，所请求的集群支持以下研究：(1)在异构多核硬件环境中设计和优化新型数据流管理系统架构；(2)粗粒度分子模拟方法，可以精确模拟大尺度原子系统；(3)部署在策略组合和运行时性能方面均出色的安全策略的新系统；(4)高效节能、安全的硬件系统建模与设计；(5)利用模式理论对活动进行自动解释；(6)快速大规模集群；(7)生物医学图像数据模式识别。这个项目的智力价值来自于单个项目的创新，以及它在未来可能发芽的潜在跨学科思想。该基础设施有望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，在研究人员之间建立一个协作网络。此外，该集群预计将通过在整个USF系统中的研究人员及其合作者之间正在进行和计划的研究项目影响十几个应用领域。教育和研究的直接利益也将通过项目、教学和培训的应用方面扩大到更大的社区。该集群的项目成果和媒体内容将在广受欢迎的USF Engineering EXPO活动中展示，该活动旨在教育和激励K-12学生学习数学、科学、工程和技术科目。

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.