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.

该项目是由云云的成功部署的成功部署的动机：如何在云上部署HPC Analytics应用程序？这两个Escience应用程序和HPC Analytics应用程序都用TERA级或PETA尺度数据操作，并且需要访问昂贵的计算资源。但是，HPC Analytics应用程序具有多种不同的特征，例如复杂的数据访问模式和兴趣区域，这对其在云中的采用中构成了新的挑战。该项目的目的是开发一个数据语义知识框架，以启用云的HPC分析。这样的框架由三个组成部分组成； 1）具有用于开发高效率分析应用程序的数据语义意识的MAPREDUCE API，2）配备了数据 - 语音知识HPC接口的翻译层，以及3）数据附属性数据放置方案。预计，通过具有成本效益的科学数据处理，对经济影响的高生产率得到了显着改善。向社区提供开源软件，以加快21世纪的科学发现过程，以宇宙学分析领域（例如天文学，天文学，彩色动力学，生物学能力效益等）与许多HPC分析领域相比，从而加快了与许多教育效益等。通过整合到未来的grid和能源部的科学计算云。