Foundations of GPU Computing with CUDA

Modern computational challenges in artificial intelligence, data science, and physics simulations demand performance that traditional processors can no longer deliver alone. This text-only course introduces you to the core concepts of parallel programming using GPUs, enabling you to accelerate your software significantly. You will transition from sequential programming mindsets to parallel thinking, mastering the fundamental execution model of modern graphics hardware.\n\nThrough clear written explanations and structured code walk-throughs, you will build a solid theoretical and practical foundation in high-performance computing. You will learn how to design algorithms that run thousands of threads simultaneously and how to avoid common pitfalls in parallel development.\n\nWhat you'll learn:\n- Understand the architectural differences between CPUs and GPUs and when to use each\n- Master the CUDA execution model, including threads, blocks, and grid organization\n- Write and launch custom parallel kernels to perform vector and matrix operations\n- Manage memory hierarchies efficiently by utilizing global, shared, and registers\n- Avoid common concurrency issues like race conditions and uncoalesced memory access\n- Profile and analyze parallel code to locate and resolve performance bottlenecks\n\nWe start with the essential hardware terminology and basic parallel concepts before moving into memory management and optimization techniques. This course is designed for software developers and students with a basic understanding of C or C++ who want to enter the field of accelerated computing. No prior GPU programming experience is required.\n\nStart your journey into high-performance parallel programming today.