Applications of Parallel Computers¶
This course is offered by UC Berkeley CS267
Syllabus¶
CS267 was originally designed to teach students how to program parallel computers to efficiently solve challenging problems in science and engineering, where very fast computers are required either to perform complex simulations or to analyze enormous datasets.
CS267 is intended to be useful for students from many departments and with different backgrounds, although we will assume reasonable programming skills in a conventional (non-parallel) language, as well as enough mathematical skills to understand the problems and algorithmic solutions presented.
Students in CS267 will get an overview of the parallel architecture space, gain experience using some of the most popular parallel programming tools, and be exposed to a number of open research questions. The lectures will also cover a broad set of parallelization strategies for applications covering numerical simulation and data analysis to machine learning.
Lecture Video¶
Lectures will be recorded and posted on youtube here (You need to login with your @berkeley.edu
gsuite).
Personal Motivation to take this class¶
I was requested to solve a MTP related problem at Microsoft, but actually, I have no idea what MTP is.
Taken deeper insight into the MTP issue, I find that I even don't know what is a parallel computer.
The real routine is: Operating System -> Parallel Computing -> CCL -> MTP related.
So I decide to take this course to learn more about parallel computing.
Routine¶
The plan for organizing this course document is to first sort out Chapters 1–15.
By this point, I will have a solid grasp of the basics and background knowledge of CCL, after which I will return to work on the MTP tasks.
The remaining Chapters 16–26 will be completed later when I time is permitted.