EAGER: Tadoop: A Dual-Purpose Framework Taming the Bipolarity of Storage and Communication for High-Performance Computing and Data Analytics

EAGER：Tadoop：一个双用途框架，克服存储和通信的两极性，实现高性能计算和数据分析

• 批准号: 1432892
• 负责人: Weikuan Yu
• 金额: $ 29.81万
• 依托单位: Auburn University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2015-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1432892&HistoricalAwards=false
• 关键词: EAGER; Tadoop; Dual; Purpose; Framework

High-performance computing (HPC) providers and applications need next-generation solutions to process big data from scientific simulations. Conventional HPC systems found in national laboratories and universities are constructed based on the compute-centric paradigm while enterprise big data analytics applications prefer a data-centric paradigm such as MapReduce. Distinct architectural differences between these two paradigms demand unconventional approaches. This project takes a radically different approach to investigate key architectural components in compute-centric and data-centric paradigms, designs a transformative dual-purpose framework called Tadoop that addresses their bipolarity issues in storage and communication management, and unifies them for both HPC and enterprise analytics applications. This high-risk Tadoop framework can enable a transformative data infrastructure for both HPC and data analytics applications and lead to broader impact in several aspects, such as demonstrating the transformation of existing HPC infrastructures into dual-purpose systems for computing and analytics, improving computer science curricula and instruction effectiveness, strengthening multidisciplinary data analytics research, releasing open-source software code, and transferring technologies for commercial service.

高性能计算（HPC）提供商和应用程序需要下一代解决方案来处理来自科学模拟的大数据。 在国家实验室和大学中发现的传统HPC系统是基于以计算为中心的范式构建的，而企业大数据分析应用程序更喜欢以数据为中心的范式，如MapReduce。 这两种范式之间的明显架构差异需要非传统的方法。 该项目采取了一种截然不同的方法来研究以计算为中心和以数据为中心的范式中的关键架构组件，设计了一个名为Tadoop的变革性双重用途框架，可解决存储和通信管理中的双极问题，并将其统一用于高性能计算和企业分析应用程序。 这种高风险的Tadoop框架可以为HPC和数据分析应用程序提供变革性的数据基础设施，并在几个方面产生更广泛的影响，例如展示现有HPC基础设施向计算和分析两用系统的转型，改进计算机科学课程和教学效率，加强多学科数据分析研究，发布开源软件代码，并为商业服务转让技术。