About Introduction to High Performance and Parallel Computing course
This course introduces the fundamentals of high-performance and parallel computing. It is designed for scientists, engineers, researchers, and anyone who wants to develop the software skills needed to work in parallel software environments. These skills include big data analytics, machine learning, parallel programming, and optimization. We will cover the basics of working in a Linux environment and bash scripting, all the way to high-performance computing and parallel code. We recommend that you be familiar with Fortran 90, C++, or Python for some of the programming assignments.
Upon completion of this course, you will be familiar with: *The components of a high-performance distributed computing system *Types of parallel programming models and the situations in which they can be used *High-throughput computing *Shared-memory parallelism *Distributed-memory parallelism *Navigating a typical Linux-based HPC environment *Evaluating and analyzing application scalability, including weak and strong scaling *Processing quantification This course can be taken for academic credit toward CU Boulder’s Master of Science in Data Science (MS-DS) program, offered on Coursera. The MS-DS is an interdisciplinary degree that features faculty from CU Boulder’s Departments of Applied Mathematics, Computer Science, Information Science, and other departments. The MS-DS program, which is outcomes-based and requires no application, is ideal for individuals with broad educational and/or professional experience in computer science, computer science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
