Collaborative Research: CSR-SMA+AES: Pro-Active Runtime Health Enhancement of Large-Scale Parallel Systems Using PROGNOSIS

合作研究：CSR-SMA AES：使用 PROGNOSIS 主动增强大规模并行系统的运行时健康状况

• 批准号: 0614976
• 负责人: Yanyong Zhang
• 金额: $ 23.8万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-15 至 2010-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0614976&HistoricalAwards=false
• 关键词: Collaborative; Research; CSR; SMA+AES; Pro

Large scale parallel systems are critical to take on the challenges imposed by highly demanding applications of critical importance. Pushing the limits of hardware and software technologies to extract the maximum performance can increase their susceptibility to failures. This arises as a consequence of growing hardware transient errors, hardware device failures, and software complexity. These failures can have substantial consequences on system performance, and add to the costs of maintenance/operation, thereby putting at risk the very motivation behind deploying these large scale systems. Rather than treat failures as an exception and takereactive remedies, this project intends to anticipate their occurrence and take pro-active runtime measures to hide their impact.This research is expected to make three broad contributions towardsdeveloping a runtime fault-tolerance infrastructure.The first set of contributions is on collecting and analyzingsystem events from an actual BlueGene/L system over anextended period of time. The second set of contributions are models foronline analysis and prediction of evolving failure data.The third set of contributions are on failure-aware parallel job scheduling and checkpointing. On the educational front, in addition to enhancing graduate curriculum and research, this project intends to involve undergraduate students and women. The tools developed in this project and the related results will be made available in public domain and published in leading journals/conferences. In addition, the PIs will also push these tools to be incorporated on actual systems, to enhance their fault-toleranceabilities.

大规模平行系统对于应对至关重要的高度要求应用所面临的挑战至关重要。推动硬件和软件技术的限制提取最高性能可以提高其失败的敏感性。这是由于硬件瞬态错误，硬件设备故障和软件复杂性而引起的。 这些故障可能会对系统性能产生重大影响，并增加维护/操作的成本，从而使他们处于部署这些大规模系统背后的动力。该项目并没有将失败视为例外和交管反应疗法，而是要预测其发生并采取积极主动的运行时措施来隐藏其影响。这项研究有望为开发运行时耐受竞争力基础架构提供三个广泛的贡献。对收集和分析实际蓝色系统的第一组贡献是针对蓝色系统的首次贡献。第二组贡献是模型的Foronline分析和对不断发展的故障数据的预测。第三组贡献是在失败感知的并行作业计划和检查点上。在教育方面，除了增强研究生课程和研究外，该项目还打算让本科生和女性参与。该项目中开发的工具和相关结果将在公共领域提供，并在领先的期刊/会议上发布。此外，PI还将推动这些工具将其纳入实际系统，以增强其断层耐受性。