Class Central Tips
After completing this course, you will familiar with:
*The components of a high-performance distributed computing system
*Types of parallel programming models and the situations in which they might be used
*High-throughput computing
*Shared memory parallelism
*Distributed memory parallelism
*Navigating a typical Linux-based HPC environment
*Assessing and analyzing application scalability including weak and strong scaling
*Quantifying the processing, data, and cost requirements for a computational project or workflow
This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.