This course is a graduate-level introduction to scalable parallel algorithms. “Scale” really refers to two things: efficient as the problem size grows, and efficient as the system size (measured in numbers of cores or compute nodes) grows. To really scale your algorithm in both of these senses, you need to be smart about reducing asymptotic complexity the way you’ve done for sequential algorithms since CS 101; but you also need to think about reducing communication and data movement. This course is about the basic algorithmic techniques you’ll need to do so. The techniques you’ll encounter cover the main algorithm design and analysis ideas for three major classes of machines: for multicore and manycore shared memory machines, via the work-span model; for distributed memory machines like clusters and supercomputers, via network models; and for sequential or parallel machines with deep memory hierarchies (e.g., caches). You will see these techniques applied to fundamental problems, like sorting, search on trees and graphs, and linear algebra, among others. The practical aspect of this course is implementing the algorithms and techniques you’ll learn to run on real parallel and distributed systems, so you can check whether what appears to work well in theory also translates into practice. (Programming models you’ll use include OpenMP, MPI, and possibly others.)
More information is available on the CSE 6220 course website.
This course counts towards the following specialization(s):
This course is a graduate-level introduction to parallel computing. Its goal is to give you the foundations to develop, analyze, and implement parallel and locality-efficient algorithms and data structures.
Note: Sample syllabi are provided for informational purposes only. For the most up-to-date information, consult the official course documentation.
You can view the lecture videos for this course here.
Before Taking This Class...
Suggested Background Knowledge
Please review the course readiness survey for CSE 6220. If you are unable to answer any of them you may want to refresh your knowledge of the area prior to taking this course.
It is also recommended that you:
- Have taken CS 6515: Intro to Grad Algorithms or an undergraduate algorithms class
- Have programming experience in a “low- level” “high-level” language like C or C++ (for the programming assignments)
- Have experience using command line interfaces in *nix environments (e.g., Unix, Linux
Technical Requirements and Software
- Browser and connection speed: An up-to-date version of Chrome or Firefox is strongly recommended. We also support Internet Explorer 9 and the desktop versions of Internet Explorer 10 and above (not the metro versions). 2+ Mbps is recommended; the minimum requirement is 0.768 Mbps download speed.
- Operating system:
- PC: Windows XP or higher with latest updates installed
- Mac: OS X 10.6 or higher with latest updates installed
- Linux: any recent distribution that has the supported browsers installed
- We may also elect to provide virtual machines with a standardized environment, though most of the assignments can be completed by directly logging into the HPC resource we will provide via secure shell (ssh).
All Georgia Tech students are expected to uphold the Georgia Tech Academic Honor Code. This course may impose additional academic integrity stipulations; consult the official course documentation for more information.