Prerequisites: CS 160 with a grade of “C-” or better; graduate standing; or consent of instructor
Description: Introduces the popular CUDA based parallel programming environments based on Nvidia GPUs. Covers the basic CUDA memory/threading models. Also covers the common data-parallel programming patterns needed to develop a high-performance parallel computing applications. Examines computational thinking; a broader range of parallel execution models; and parallel programming principles.
Cross-listing: Cross-listed with EE 217.
Credit: May be taken Satisfactory (S) or No Credit (NC) with consent of instructor and graduate advisor.