Lecture
This lecture introduces the basics of GPUs, focusing on their architecture, parallelism models, and CUDA programming. It covers the history of GPUs, the evolution of multithreading techniques, and the transition from vector to SIMD processors. The lecture explains the importance of GPUs in modern computing, especially for machine learning and parallel applications. It details GPU memory allocation, data transfer between CPU and GPU, and thread synchronization. The instructor discusses the organization of threads into blocks and grids, the use of global functions in CUDA kernels, and the significance of thread synchronization using syncthreads(). The lecture concludes with a review of GPU fundamentals and CUDA programming principles.