Collaborative Research: CPA-CPL-T: An Effective Automatic Parallelization Framework for Multi-Core Architectures

合作研究：CPA-CPL-T：一种有效的多核架构自动并行化框架

• 批准号: 0811457
• 负责人: Jagannathan Ramanujam
• 金额: $ 25万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0811457&HistoricalAwards=false
• 关键词: Collaborative; Research; CPA; CPL; Effective

Data protection and recovery have become increasing important as business, education, and government depend more and more on digital information. Failure events do occur such as virus attacks, user errors, defective software/firmware, hardware faults, and site failures etc that cause data damage. To ensure business continuity and minimize loss, data storage systems need data protection and recovery techniques. However, existing technologies have severe limitations and unable to recover data in many situations. This project aims at studying and understanding how data recovery is done in existing data storage systems, and designing new architectures that will overcome the limitations of existing technologies. In order to study and understand the existing storage architectures, a new mathematical formulation will be developed to model and analyze capabilities and limitations of the storage architectures. This mathematical model provides a rigorous tool for researchers and practitioners to investigate and understand storage system architectures. Based on the new mathematical model, a class of new data storage system architectures will be designed that will have the maximum data recoverability. The new storage architectures make it possible for organizations of different sizes to have a cost-effective data storage that provides high data availability and allows quick data recovery upon failures. In addition to the theoretical study, experimental prototypes will be developed and implemented to demonstrate the feasibility, performance, reliability, and data recoverability of newly designed storage architectures. Furthermore, the project includes an education component that advocates a shift of emphasis from CPU-centric computer engineering (CE) curriculum to data-centric CE curriculum. The new curriculum provides CE students with in depth knowledge of data processing, data communication, and data storage.

随着商业、教育和政府越来越依赖于数字信息，数据保护和恢复变得越来越重要。故障事件确实会发生，如病毒攻击、用户错误、有缺陷的软件/固件、硬件故障和站点故障等，导致数据损坏。为了保证业务的连续性和减少损失，数据存储系统需要数据保护和恢复技术。然而，现有技术存在严重的局限性，在许多情况下无法恢复数据。该项目旨在研究和理解如何在现有的数据存储系统中进行数据恢复，并设计新的架构，以克服现有技术的限制。为了研究和理解现有的存储体系结构，将开发一个新的数学公式来建模和分析存储体系结构的能力和局限性。这个数学模型为研究人员和从业者研究和理解存储系统架构提供了一个严格的工具。基于新的数学模型，设计了一类具有最大数据可恢复性的新型数据存储系统架构。新的存储架构使不同规模的组织能够拥有具有成本效益的数据存储，提供高数据可用性，并允许在故障时快速恢复数据。除了理论研究外，还将开发和实施实验原型，以证明新设计的存储架构的可行性、性能、可靠性和数据可恢复性。此外，该项目还包括一个教育组件，倡导将重点从以cpu为中心的计算机工程（CE）课程转向以数据为中心的CE课程。新课程为CE学生提供数据处理、数据通信和数据存储的深入知识。