CAREER: High-Performance Computing for Computational Science and Engineering

本职业发展计划的主要重点是在计算科学和工程领域的高性能计算研究和教育项目的协调发展。研究计划：高性能计算的主要目标是高性能、可伸缩性和可移植性。为了实现现代科学和工程应用的这些目标，需要新的硬件范例，新的软件方法和新的数值算法。此外，硬件、软件、算法和应用程序之间的相互依赖关系对性能、可伸缩性和可移植性具有重要影响，并且必须很好地理解。这项工作是一项包容性的努力，旨在为科学和工程应用的高性能计算创建一个实质性的、统一的理解基础，从而可以广泛应用。这项工作的具体目标是：(1)开发和分析能够利用现代计算架构和操作系统（如对称多处理器、操作系统和千兆网络）的高性能、可移植的计算内核和数字库；(2)为高性能计算和计算科学与工程应用开发和分析基于对象和面向对象的原型可扩展软件库；(3)开发和分析新的算法，以最大限度地提高平稳和时间相关问题的可扩展性；(4)将新的算法和软件库应用于真实的、实质性的计算科学和工程应用。教育计划：由于计算科学和工程的重要性日益增加，计算机科学家（实际上，科学家和工程师一般来说）理解高性能计算的复杂性和内在关系是很重要的。在圣母大学的计算机科学与工程项目中，一门关于数值计算的研究生课程已经被开发并纳入研究生核心课程。在本课程的基础上，将继续引入新的高性能计算研究生课程。在本科阶段，正在引入高性能计算的高级选修课，并将继续努力支持和指导本科生的研究。

The primary emphasis of this Career Development Plan is the coordinated development of research and education programs in high-performance computing for computational science and engineering. Research Plan: The main goals of high-performance computing are high-performance, scalability, and portability. To attain these objectives for modern scientific and engineering applications, new hardware paradigms, new software methodologies, and new numerical algorithms are needed. Moreover, the interdependencies between hardware, software, algorithm, and application have an essential bearing on performance, scalability, and portability and must also be well understood. This work is an inclusive effort to create a substantial, unified base of understanding about high-performance computing for scientific and engineering applications that can be broadly applied. The specific objectives of this work are to: (1) Develop and analyze high-performance, portable, computational kernels and numerical libraries that are capable of exploiting modern computing architectures and operating systems, such as symmetric multiprocessors, operating systems, and gigabit networks; (2) Develop and analyze prototype object-based and object-oriented scalable software libraries for high-performance computing and for computational scientific and engineering applications; (3) Develop and analyze new algorithms for maximizing the scalability of stationary and time-dependent problems; (4) Apply the new algorithms and software libraries to real, substantial, computational science and engineering applications. Education Plan: Because of the increasing importance of computational science and engineering, it is important that computer scientists (and indeed, scientists and engineers in general) understand the intricacies and inter-relationships inherent to high-performance computing. A graduate course in numerical computing has already been developed and incorporated into the graduate core curriculum in the Computer Science and Engineering program at the University of Notre Dame. Building on this course, new graduate-level courses in high-performance computing will continue to be introduced. At the undergraduate level, a senior elective in high-performance computing is being introduced, and there will be continued effort to support and direct undergraduate research.