CUDA Programming Basics: GPU Parallelism and Vector Addition

Modern computing challenges require the massive parallel processing power of GPUs, yet transitioning from CPU-based programming can feel daunting. This course simplifies that transition by focusing on the fundamental building blocks of parallel architecture. Through clear written explanations and detailed code snippets, you will transition from sequential thinking to parallel execution. You will understand how to harness GPU cores, manage device memory, and write clean, efficient CUDA code to accelerate mathematical computations. What you'll learn: - Understand the core architecture of GPUs and how they differ from traditional CPUs - Configure CUDA grid, block, and thread dimensions for optimal parallel execution - Allocate and manage GPU memory using modern Unified Memory and traditional techniques - Write and execute a complete vector addition kernel from scratch - Implement basic CUDA error handling and debugging practices for parallel code - Analyze thread indexing to map parallel threads to data elements correctly The course begins with essential hardware concepts and terminology before guiding you step-by-step through writing, compiling, and optimizing your first parallel programs. It is designed for beginner programmers and computer science students who want to explore high-performance computing without any prior GPU experience. Start reading today and unlock the power of GPU-accelerated computing.