Introduction to GPU Architecture and Parallel Programming

Modern computational challenges in data science, artificial intelligence, and scientific computing demand massive processing power that traditional processors cannot deliver alone. Understanding how GPUs work and how to program them is a crucial skill for modern software development. This text-based course guides you from a CPU-centric mindset to thinking in parallel, enabling you to read, design, and optimize code for high-performance hardware. In this course, you will learn to: 1. Understand the core differences between CPU and GPU hardware architectures and memory hierarchies. 2. Master the fundamentals of parallel programming using CUDA syntax, grids, blocks, and thread structures. 3. Manage GPU memory configurations efficiently, including global, shared, and modern unified memory systems. 4. Implement essential parallel algorithms such as reduction, scanning, and matrix operations. 5. Analyze GPU code execution to identify and resolve performance bottlenecks. 6. Explore modern parallel computing concepts including cooperative groups and hardware acceleration features. The course begins with foundational hardware terminology and execution models, progressively walking you through parallel programming patterns and memory optimization techniques. It is designed for beginners to parallel computing, software developers, and students with basic C/C++ knowledge, requiring no prior GPU programming experience. Start your journey into high-performance computing and learn to harness the power of parallel processing today.