GPU Architecture and Parallel Programming with CUDA

Modern computing challenges demand massive computational power, and traditional processors often fall short. Transitioning to parallel programming allows you to leverage the immense power of graphics processing units (GPUs) for complex mathematical and data-intensive tasks. This text-based course guides you through the fundamental principles of GPU architecture and parallel computing. You will learn how to write, analyze, and optimize parallel code using CUDA. What you'll learn: 1. Understand foundational GPU architecture concepts and how they differ from traditional CPUs. 2. Write parallel programs using CUDA syntax, managing threads, blocks, and grids. 3. Configure GPU memory hierarchies, including global, shared, and local memory, for optimal performance. 4. Apply common parallel computing patterns such as reduction and vector operations. 5. Practice debugging and analyzing basic CUDA code snippets to identify performance bottlenecks. This course begins with essential terminology, basic hardware concepts, and foundational definitions before moving into practical code walkthroughs. You will progress from core theory to reading and understanding structured parallel algorithms. Designed for programmers and computer science students new to parallel programming, this course requires only basic C or C++ knowledge and no prior GPU experience. Start reading today to unlock the power of high-performance parallel computing.