CAREER: Dependable High Performance Scientific Computing at Extreme Scale via Algorithmic Fault Tolerance

职业：通过算法容错实现大规模可靠的高性能科学计算

• 批准号: 1305624
• 负责人: Zizhong Chen
• 金额: $ 45.45万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1305624&HistoricalAwards=false
• 关键词: CAREER; Dependable; Performance; Scientific; Computing

Extreme scale high-end computing platforms are expected to be available before 2020 and will have 100 million to 1 billion CPU cores. Due to the large number of components in these platforms, the probability that errors occur during the execution of an extreme scale application is expected to be much higher than observed today. The goal of this CAREER research project is to develop highly efficient techniques to detect, locate, and correct both soft and hard errors according to the specific characteristics of an algorithm. The target algorithms include (1) Krylov subspace methods for solving sparse linear systems and eigenvalue problems; (2) Direct methods for solving dense linear systems and eigenvalue problems; and (3) Newton's method for solving systems of non-linear equations. This project will create significant education outcomes by integrating the following four components: (1) establishing a supercomputing research laboratory to support senior design projects and REU, enhance graduate education and research, and demonstrate highly dependable applications on high-end computing platforms; (2) enriching the teaching of both undergraduate and graduate courses by integrating fault tolerance and high performance computing into the courses; (3) increasing minority students involvement by encouraging minority students to pursue graduate degrees in computing; and (4) offering free workshops to K-12 teachers and students.

超大规模高端计算平台预计将在2020年之前问世，其CPU核数将达到1亿至10亿个。由于这些平台中有大量组件，因此在执行极端规模应用程序期间发生错误的概率预计将比目前观察到的要高得多。这个CAREER研究项目的目标是根据算法的具体特征开发高效的技术来检测、定位和纠正软错误和硬错误。目标算法包括：(1)求解稀疏线性系统和特征值问题的Krylov子空间方法；(2)求解密集线性系统和特征值问题的直接方法；(3)求解非线性方程组的牛顿方法。该项目将通过整合以下四个组成部分，创造显著的教育成果：(1)建立一个超级计算研究实验室，支持高级设计项目和REU，加强研究生教育和研究，并在高端计算平台上展示高度可靠的应用；(2)通过将容错和高性能计算融入到课程中，丰富本科和研究生课程的教学；(3)通过鼓励少数族裔学生攻读计算机研究生学位，提高少数族裔学生的参与度；(4)为K-12教师和学生提供免费讲习班。