This course is an introduction to GPU computing - the practice (and art) of programming the massively parallel processors that are now ubiquitous in workstations and laptops - for more general tasks beyond the rendering context for which they were originally developed. In the process we will learn the details of GPU architecture and programming models, a significant number of parallel algorithms, techniques, and patterns, and how to maximize performance in the heterogeneous computing environment. We will do this through examples and assignments in various frameworks/APIs including CUDA C/C++, OpenCL, MPI, and Thrust, among others. This is a practical course - we will be compiling from the very first class - and encourage you to bring cross-disciplinary problems as potential projects.

There will be five 'regular' programming assignments and one final project; most can be written in C or C++ within the framework provided.

Late assignments lose 10% each day they are late. They can be a maximum of 5 days late, after which they receive no credit.

On Collaboration: Discussion of algorithms is encouraged, as well as the sharing of drawings and other representations of problems and the like. What is not permitted is the sharing or acquisition of code in any form. Any material from an outside source must be explicitly acknowledged