Scaling CUDA C++ Applications to Multiple Nodes

As computational demands and dataset sizes grow, single-GPU acceleration is often no longer enough. Transitioning your applications to run across multiple GPUs and network nodes is the key to unlocking true high-performance computing power. This text-based course guides you through the fundamental patterns, architectural concepts, and programming models required to scale your GPU-accelerated applications. You will transition from writing single-device code to coordinating complex, multi-node parallel execution. What you'll learn: - Understand the core architecture of multi-GPU and multi-node distributed systems. - Implement multi-GPU communication using CUDA-aware Message Passing Interface (MPI). - Apply collective communication patterns to synchronize data efficiently across separate nodes. - Configure peer-to-peer transfers and leverage GPUDirect RDMA concepts to bypass host memory bottlenecks. - Practice writing clean, scalable CUDA C++ code through structured written explanations and step-by-step code walkthroughs. This course begins with foundational definitions of distributed memory systems, network topologies, and multi-GPU communication basics. You will then progress through written explanations of communication protocols, peer-to-peer transfers, and multi-node orchestration. This course is designed for developers who have a basic understanding of single-GPU CUDA C++ and want to learn how to scale their applications. No prior experience with multi-node clusters or MPI is required. Start scaling your parallel computing skills today.