Multi-GPU Programming with CUDA C++

As datasets and computational demands grow, a single graphics card is often not enough to handle intensive processing tasks. Learning how to distribute workloads across multiple processors is an essential skill for modern high-performance computing. This text-only course guides you through the foundational concepts of multi-GPU programming, enabling you to coordinate multiple devices, manage memory efficiently, and write clean CUDA C++ code to accelerate your parallel algorithms. What you'll learn: - Understand the core architecture of multi-GPU systems and how devices communicate. - Configure and launch CUDA kernels across multiple graphics processors simultaneously. - Manage memory distribution using unified memory and explicit peer-to-peer transfers. - Apply modern C++ standards to write clean, maintainable, and efficient parallel code. - Coordinate stream synchronization and event handling across different devices. - Identify and resolve common performance bottlenecks in multi-device applications. The course starts with essential multi-GPU terminology and hardware concepts before moving into practical code walkthroughs and implementation strategies. You will progress from basic multi-device setups to advanced memory copy techniques and synchronization patterns through structured written explanations. This course is designed for developers who have a basic understanding of single-device CUDA C++ and want to scale their skills to multi-GPU environments. No prior multi-device programming experience is required. Start reading today to unlock the full power of parallel computing across multiple graphics processors.