XPS: SDA: Collaborative Research: A Scalable and Distributed System Framework for Compute-Intensive and Data-Parallel Applications
XPS:SDA:协作研究:用于计算密集型和数据并行应用的可扩展分布式系统框架
基本信息
- 批准号:1337131
- 负责人:
- 金额:$ 37.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2013
- 资助国家:美国
- 起止时间:2013-09-01 至 2017-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Whereas traditional high-performance computing (HPC) applications are computationally intensive, recent HPC applications require more data-intensive analysis and visualization to extract knowledge. In many cases, these applications execute the same computational algorithm as in the past (e.g., parallel search or parallel rendering) but now must do so for significantly larger data sets. For example, the life sciences, along with the cross-cutting area of scientific visualization, constitute an emerging category of HPC applications that not only perform sophisticated calculations but also ingest a sea of data. Running these new HPC data-parallel applications on today's computing platforms imposes new challenges and demands additional functionality.However, today's HPC platforms still adopt a compute-centric model and do not handle these new challenges well. Such a model often moves a large amount of data to various parallel computational processes. Consequently, long CPU wait times for I/O to complete and enormous data-movement overhead become major stumbling blocks to high performance and scalability. This project encompasses the creation of a scalable cross-layer software framework to enable both computationally intensive and data-intensive parallel HPC applications to run on distributed file systems. This framework consists of two interwoven research tasks: (1) an adaptive, data locality-aware, middleware system that dynamically schedules compute processes to access local data by monitoring physical data locations and (2) a framework that captures the computation and data I/O processing relationship from parallel applications and coordinates the scheduling of the corresponding process and I/O execution for maximum parallel efficiency. The success of this project contributes enhanced productivity and return on investment on HPC resources via the elimination of both CPU wait time and network transfer of frequently accessed data in scientific applications. An open-source, sustainable, and reusable software framework is delivered to speed-up the discovery and innovation process in areas such as bioinformatics, climate, high-energy physics, cosmology, astrophysics, and chromodynamics. The synergy in the two proposing institutions, Virginia Tech and the University of Central Florida, and their collaborating DOE national laboratories, will catalyze new and beneficial perspectives in the graduate education of students and prepare a 21st-century workforce in HPC.
传统的高性能计算(HPC)应用程序是计算密集型的,而最近的HPC应用程序需要更多的数据密集型分析和可视化来提取知识。在许多情况下,这些应用程序执行与过去相同的计算算法(例如,并行搜索或并行呈现),但现在必须对大得多的数据集这样做。例如,生命科学,沿着科学可视化的交叉领域,构成了一个新兴的HPC应用类别,不仅执行复杂的计算,而且还摄取大量数据。在当今的计算平台上运行这些新的HPC数据并行应用程序带来了新的挑战,并需要额外的功能。然而,当今的HPC平台仍然采用以计算为中心的模型,无法很好地应对这些新挑战。这样的模型通常将大量数据移动到各种并行计算过程。因此,CPU等待I/O完成的时间过长以及巨大的数据移动开销成为高性能和可伸缩性的主要障碍。该项目包括创建一个可扩展的跨层软件框架,以使计算密集型和数据密集型并行HPC应用程序能够在分布式文件系统上运行。该框架包括两个交织的研究任务:(1)一个自适应的,数据本地感知的,中间件系统,动态调度计算过程访问本地数据通过监视物理数据的位置和(2)一个框架,捕获计算和数据I/O处理的关系,从并行应用程序和协调调度相应的进程和I/O执行最大的并行效率。该项目的成功通过消除CPU等待时间和科学应用中频繁访问数据的网络传输,提高了生产力和HPC资源的投资回报。一个开源的,可持续的,可重用的软件框架被交付,以加快在生物信息学,气候,高能物理学,宇宙学,天体物理学和色动力学等领域的发现和创新过程。弗吉尼亚理工大学和中央佛罗里达大学这两个提议机构及其合作的能源部国家实验室的协同作用,将促进学生研究生教育的新的有益观点,并为21世纪的HPC劳动力做好准备。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Wuchun Feng其他文献
Wuchun Feng的其他文献
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{{ truncateString('Wuchun Feng', 18)}}的其他基金
Collaborative Research: Workshop Series on Sustainable Computing
协作研究:可持续计算研讨会系列
- 批准号:
2125999 - 财政年份:2021
- 资助金额:
$ 37.5万 - 项目类别:
Standard Grant
RAPID: Higher Accuracy and Availability of COVID-19 Testing and Monitoring via Post-CT Image Boosting and Analysis
RAPID:通过 CT 后图像增强和分析提高 COVID-19 测试和监测的准确性和可用性
- 批准号:
2031215 - 财政年份:2020
- 资助金额:
$ 37.5万 - 项目类别:
Standard Grant
RAPID: A Computational Deep-Learning Approach for Fast, Accurate CT Testing and Monitoring of COVID-19
RAPID:一种计算深度学习方法,可快速、准确地进行 CT 测试和 COVID-19 监测
- 批准号:
2027607 - 财政年份:2020
- 资助金额:
$ 37.5万 - 项目类别:
Standard Grant
Phase-I IUCRC Virginia Tech: Center for Space, High-performance, and Resilient Computing (SHREC)
第一阶段 IUCRC 弗吉尼亚理工大学:空间、高性能和弹性计算中心 (SHREC)
- 批准号:
1822080 - 财政年份:2018
- 资助金额:
$ 37.5万 - 项目类别:
Continuing Grant
NSF XPS Workshop for Exploiting Parallelism and Scalability
NSF XPS 利用并行性和可扩展性研讨会
- 批准号:
1451021 - 财政年份:2014
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$ 37.5万 - 项目类别:
Standard Grant
EAGER: Collaborative Research: Democratizing the Teaching of Parallel Computing Concepts
EAGER:协作研究:并行计算概念教学的民主化
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1353786 - 财政年份:2013
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$ 37.5万 - 项目类别:
Standard Grant
BIGDATA: Mid-Scale: DA: Collaborative Research: Genomes Galore - Core Techniques, Libraries, and Domain Specific Languages for High-Throughput DNA Sequencing
大数据:中规模:DA:协作研究:基因组丰富 - 高通量 DNA 测序的核心技术、库和领域特定语言
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1247693 - 财政年份:2013
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$ 37.5万 - 项目类别:
Standard Grant
CiC (RDDC): Commoditizing Data-Intensive Biocomputing in the Cloud
CiC (RDDC):云中数据密集型生物计算的商品化
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1048253 - 财政年份:2011
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$ 37.5万 - 项目类别:
Standard Grant
MRI-R2: Acquisition of a Heterogeneous Supercomputing Instrument for Transformative Interdisciplinary Research
MRI-R2:获取用于变革性跨学科研究的异构超级计算仪器
- 批准号:
0960081 - 财政年份:2010
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$ 37.5万 - 项目类别:
Standard Grant
CSR: Small: Collaborative Research: Hybrid Opportunistic Computing for Green Clouds
CSR:小型:协作研究:绿色云的混合机会计算
- 批准号:
0916719 - 财政年份:2009
- 资助金额:
$ 37.5万 - 项目类别:
Continuing Grant
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