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，例如病毒攻击、用户错误、有缺陷的软件/固件、硬件故障和站点故障等，从而导致数据损坏。为了确保业务连续性并最大程度地减少损失，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。该项目旨在研究和了解现有数据存储系统中的数据恢复是如何完成的，并设计新的架构来克服现有技术的局限性。为了研究和理解现有的存储架构，将开发一种新的数学公式来建模和分析存储架构的功能和局限性。该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严格的工具。基于新的数学模型，将设计一类新的数据存储系统架构，使其具有最大的数据可恢复性。新的存储架构使不同规模的组织能够拥有经济高效的数据存储，提供高数据可用性并允许在发生故障时快速恢复数据。除了理论研究之外，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部分，提倡将重点从以 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学生提供了数据处理、数据通信和数据存储的深入知识。