CDI Type II: Scientific Computation for Astronomy, Neurobiology and Chemistry using Graphics Processing Units and Solid-State Storage

CDI 类型 II：使用图形处理单元和固态存储进行天文学、神经生物学和化学的科学计算

• 批准号: 0835713
• 负责人: Hanspeter Pfister
• 金额: $ 199.39万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-10-01 至 2013-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0835713&HistoricalAwards=false
• 关键词: CDI; Type; II; Scientific; Computation

TECHNICAL ABSTRACTData-intensive science requires tera- and peta-scale computing. Hardware demands are high, and software, from system to application level, is highly specialized. As a result, scientific investigation is constrained. New and more accessible models for large-scale computing are required. The proposed program seeks to leverage new off-the-shelf computing technologies to develop concepts and tools (e.g., programming strategies, general-purpose and domain-specific libraries) to enable practical and transparent Scalable Heterogeneous Computing (SHC). Results will be applied to three cutting-edge challenges in radio astronomy, quantum chemistry, and neuroscience. These three applications share the need to process massive data streams. They broadly span a parameter space of processing challenges, defined by complexity in computation, data volume, and throughput. Addressing these requires scalable algorithms for massive datasets, systems with high-bandwidth memory access, and the ability to process high-throughput data streams. Proposed SHC strategies, tools, and optimizations would develop, for general scientific computing, use of massively parallel graphics processing units (GPUs) and fast, low-power, large-volume, solid-state storage (SSS) devices that are commercially available. The tools developed will be applied to the analysis of radioastronomy data generated by the Muchison Wide-Field Array, the development of a SHC-enabled molecular quantum chemistry code, and to the Connectome project, an effort to make a complete map of the neuron connectivity of mammalian brains. Education is tightly integrated into the SHC program at the undergraduate and graduate levels. Initiatives to disseminate results will include tutorials and documented open-source libraries as well as workshops.LAY ABSTRACTScientists engaged in data-intensive research, from astronomy to neuroscience, are in desperate need of new strategies and tools. This project approaches the challenge by leveraging new off-the-shelf hardware and software technologies in unique combinations. The project will bring massively parallel graphics processing units and fast solid-state storage devices together with traditional central processing units. Project staff will develop scalable algorithms that leverage this commercially available hardware to process massive data sets and streams of data. The project will use three major scientific challenges as testbeds for development of these new approaches: a radioastronomy telescope called the Murchison Wide-Field Array; exploration of chemistry at the quantum level; and the Connectome, an effort to make a complete map of the neuron connectivity in mammalian brains. Postdoctoral researchers from astronomy, chemistry, neuroscience, and computer science will work together at Harvard?s Initiative in Innovative Computing, where they will combine experience from these domains to develop algorithms and code to broadly enable advances in science. The project will include tutorials, workshops and documented code libraries and a strong educational component.

技术摘要数据密集型科学需要万亿次和千万亿次规模的计算。硬件要求很高，软件从系统到应用程序都是高度专业化的。因此，科学研究受到限制。需要新的和更容易获得的大规模计算模型。拟议的计划旨在利用新的现成的计算技术来开发概念和工具（例如，编程策略、通用和特定领域的库），以实现实用和透明的可扩展异构计算（SHC）。结果将应用于射电天文学，量子化学和神经科学的三个前沿挑战。这三个应用程序都需要处理大量数据流。它们广泛地跨越处理挑战的参数空间，由计算、数据量和吞吐量的复杂性定义。解决这些问题需要针对大规模数据集的可扩展算法、具有高带宽内存访问的系统以及处理高吞吐量数据流的能力。 所提出的SHC策略、工具和优化将开发用于一般科学计算的大规模并行图形处理单元（GPU）和快速、低功率、大容量、固态存储（SSS）设备的使用。开发的工具将被应用于分析Muchison宽场阵列产生的射电天文学数据，开发SHC分子量子化学代码，以及Connectome项目，该项目旨在绘制哺乳动物大脑神经元连接的完整地图。教育与本科和研究生级别的SHC课程紧密结合。传播成果的举措将包括教程和有文件记录的开源图书馆以及研讨会。LAY ABSTRACT从事数据密集型研究的科学家，从天文学到神经科学，都迫切需要新的策略和工具。该项目通过利用新的现成硬件和软件技术的独特组合来应对挑战。该项目将把大规模并行图形处理单元和快速固态存储设备与传统的中央处理单元结合在一起。项目工作人员将开发可扩展的算法，利用这种商用硬件来处理大量数据集和数据流。该项目将使用三个主要的科学挑战作为开发这些新方法的试验平台：称为Murchison Wide-Field Array的射电天文望远镜;量子水平的化学探索;以及Connectome，一项旨在绘制哺乳动物大脑神经元连接完整地图的努力。来自天文学、化学、神经科学和计算机科学的博士后研究人员将在哈佛一起工作？的创新计算计划，他们将联合收割机从这些领域的经验相结合，开发算法和代码，以广泛推动科学的进步。该项目将包括教程、研讨会和记录代码库以及一个强大的教育组件。