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

这个项目的动机是成功部署的eScience应用程序的云：如何部署HPC分析应用程序的云？eScience应用程序和HPC分析应用程序都需要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分析应用程序具有几个明显的特征，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，以支持云上的HPC分析。这样一个框架由三个部分组成；1)一个具有数据语义感知的MapReduce API，用于开发高性能分析应用程序；2)一个具有数据语义感知的HPC接口的翻译层；3)一个数据亲和感知的数据放置方案。预计高生产率对经济的影响将通过具有成本效益的科学数据处理得到显著提高。向社区提供开源软件加速了21世纪任何HPC分析领域的科学发现过程，如宇宙学、天体物理学、色动力学、生物信息学等。通过整合到未来网格和能源部的科学计算云中，UCF的几个教育项目以及外部合作和社区关系的合作努力有望产生许多教育效益。

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.