GPUs: Multithreading and Architecture

This lecture covers the architecture of GPUs, focusing on multithreading concepts. It explains the SIMT programming model, GPU microarchitecture, and the reasoning behind CUDA call ordering. The lecture delves into the execution models, memory systems, and the role of GPUs in machine learning. It discusses the limitations of GPUs for ML tasks and introduces tensor cores as a solution. The future of ML in GPUs is explored, highlighting the state-of-the-art training capabilities and the shift towards alternative platforms for inference.