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Learner Reviews & Feedback for Introduction to High-Performance and Parallel Computing by University of Colorado Boulder

26 ratings
7 reviews

About the Course

This course introduces the fundamentals of high-performance and parallel computing. It is targeted to scientists, engineers, scholars, really everyone seeking to develop the software skills necessary for work in parallel software environments. These skills include big-data analysis, machine learning, parallel programming, and optimization. We will cover the basics of Linux environments and bash scripting all the way to high throughput computing and parallelizing code. 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
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1 - 7 of 7 Reviews for Introduction to High-Performance and Parallel Computing

By Jakub D

Feb 20, 2021

Amazing contents of the videos. The assignments are absolute disaster-it's such a pity.

By Marina N

Feb 20, 2021

This course needs to do some serious improvement work on the auto-grader and the exact instructions for what the auto-grader is expecting. I spent hours on incredibly stupid issues, like the Week 4 assignment needs a blank row on line four for the auto-grader to accept it. The discussion forums are full of confused students who know the material but can't get the auto-grader to pass, due to vague instructions. The video content is good and I enjoyed the teacher, but this course should really be removed from Coursera until the technical issues are fixed.

By Heino H G

Feb 5, 2021

There were simply too many technical issues with the jupyter notebook assignments. One should not have to modify hidden .config.ini files etc.

By Drew G

Feb 7, 2021

Good course. The lectures and interesting and the mini-cluster provides an excellent practice environment. The only reason I didn't give it five stars is because some of the assignment prompts are ambiguous and the autograder does not provide any helpful feedback on what you missed. I spent 3+ weeks trying to finish one assignment that should have taken a few hours. But the course is otherwise worth your while if you want a solid introduction to HPC.


Mar 18, 2021

Good for an introduction of HPC concepts, but some lectures leave a lot to be desired. In some videos the instructor is just reading out what is written on the slide, without expanding or providing more context. Instructions for some of the assignments were confusing, the submission process was buggy. Thanks to all the students that contributed in the discussion forums where pointers like "add blank line at the end", "edit .config.ini file" etc. proved very valuable. I expected more for a course I paid $ to attend.

By Michelle W

Apr 3, 2021

Great basic overview, I finally understand what IT is talking about in meetings. I also appreciate the overview of parallel computing and how to structure programs for best advantage. However, the coursera lab environment is buggy. So, I spent more time struggling to just submit the assignments, instead of understanding the assignment.

By Taegun P

Feb 16, 2021

Many of the concepts covered by the lecture are vague and you'll need to refer to extra resources to understand what it's all about. Not to mention that the homework environments are broken and descriptions are misleading.