Introduction to Parallel Programming with CUDA

This module provides an overview of the course, expectations, and resources available. It also covers the basics of GPU programming, including the use of VS Code and GitHub resources, C++ reading material, and a simple CUDA project assignment.

This module delves into kernel execution, dividing and conquering GPU algorithms, and working with threads, blocks, and grids. It includes hands-on assignments related to image processing and data search programming, along with quizzes to reinforce learning.

Module 3 focuses on host and global memory, covering Nvidia GPU device global memory, Linux CLI GPU device identification, and memory allocation. Students engage in discussions and labs to deepen their understanding of memory management.

Module 4 explores shared and constant memory on Nvidia GPUs, including allocation, image processing, and manipulation assignments. Students engage in discussions and labs to reinforce their understanding of shared and constant memory.

This module covers CUDA GPU device register memory, memory evaluation, and analysis assignments. It includes quizzes and discussions to solidify students' understanding of register memory on GPUs.