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

极端规模的高端计算平台预计将在2020年之前推出，并将拥有1亿至10亿个CPU核心。由于这些平台中的组件数量庞大，因此在执行极端规模应用程序期间发生错误的可能性预计将远远高于目前观察到的情况。这个CAREER研究项目的目标是开发高效的技术，根据算法的具体特征来检测，定位和纠正软错误和硬错误。目标算法包括（1）用于求解稀疏线性系统和特征值问题的Krylov子空间方法;（2）用于求解稠密线性系统和特征值问题的直接方法;以及（3）用于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来创造重要的教育成果：（1）建立一个超级计算研究实验室，以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）将容错技术和高性能计算技术融入本科和研究生课程，丰富本科和研究生课程的教学内容;（3）透过鼓励少数族裔学生修读计算机研究生学位，增加少数族裔学生的参与;及（4）为幼稚园至12年级的教师及学生提供免费工作坊。

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.