CSR: Small: DSA-Cloud: Data Semantics Aware Clouds for High Performance Analytics

CSR：小型：DSA-Cloud：用于高性能分析的数据语义感知云

• 批准号: 1115665
• 负责人: Jun Wang
• 金额: $ 37.5万
• 依托单位: The University of Central Florida Board of Trustees
• 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 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1115665&HistoricalAwards=false
• 关键词: CSR; Small; DSA; Cloud; Data

This project is motivated by successful deployment of eScience applicationson clouds: how to deploy HPC analytics applications on the cloud? Both eScience applicationsand HPC analytics applications manipulate with tera-scale or peta-scale data and require access to expensive computing resources. However, HPC analytics applications bear several distinct characteristics such as complex data access patterns and interest locality, which pose new challenges to its adoption in clouds. The goal of this project is to develop a data semantics aware framework to enable HPC analytics at clouds. Such a framework is composed of three components; 1) a MapReduce API with data semantics awareness used to develop high-performanceanalysis applications, 2) a translation layer equipped with data-semantics aware HPC interfaces, and 3) a data-affinity-aware data placement scheme. It is anticipated that high productivity on theeconomic impact is significantly improved through the cost-effective scientific data processing.Delivering an open source software to the community speeds up the 21st century scientific discovery process in any HPC analytics areas such as cosmology, astrophysics, chromodynamics, bioinformatics, etc. Numerous educational benefits are expected to be generated from collaborative effort with several UCF educational projects and external collaboration and community ties through the integration into the FutureGrid, and scientific computing cloud at Department of Energy.

该项目的动机是在云上成功部署 eScience 应用程序：如何在云上部署 HPC 分析应用程序？ eScience 应用程序和 HPC 分析应用程序都处理万亿级或千万亿级数据，并且需要访问昂贵的计算资源。然而，HPC 分析应用程序具有一些独特的特征，例如复杂的数据访问模式和兴趣局部性，这对其在云中的采用提出了新的挑战。该项目的目标是开发一个数据语义感知框架，以实现云中的 HPC 分析。这样的框架由三个组件组成； 1) 具有数据语义感知功能的 MapReduce API，用于开发高性能分析应用程序；2) 配备数据语义感知 HPC 接口的转换层；3) 数据亲和力感知数据放置方案。预计通过具有成本效益的科学数据处理，可以显着提高对经济影响的高生产率。向社区提供开源软件可以加快 21 世纪任何 HPC 分析领域（例如宇宙学、天体物理学、色动力学、通过与 UCF 的多个教育项目的合作以及外部协作和社区联系，通过集成到 FutureGrid 和科学计算云，预计将产生许多教育效益。 活力。