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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.

To access the public version of this course's content, click here, then log into your Ed Lessons account. If you have not already created an Ed Lessons account, enter your name and email address, then click the activation link sent to your email, then revisit that link.

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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.

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Note: Sample syllabi are provided for informational purposes only. For the most up-to-date information, consult the official course documentation.

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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.

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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.)

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