A problem-based learning approach to GPU computing.

Compared to CPUs, modern GPUs exhibit a high ratio of computing performance per watt, and so current supercomputer designs often include multiple racks of GPUs in order to achieve high teraflop counts at minimal energy cost. GPU programming is thus becoming increasingly important, and yet it remains a challenging task. This paper describes a course in GPU programming for senior undergraduates and first-year graduates that has been taught at Clemson University annually since 2010. The course uses problem-based learning, with focus on a large, real-world problem, in particular, a system for parallel solution of partial differential equations. Although the system for solving PDEs is useful in its own right, the problem is used as a vehicle in which to explore design issues that face those attempting to achieve new levels of performance on architectures.