GPU Introduction: Performance and Programming

This lecture introduces the motivation behind using GPUs for computation, focusing on their massively parallel architecture and programmability through CUDA. It explores the historical trend of microprocessor performance and the significant advantage of GPUs in terms of theoretical GFLOPS. The lecture delves into the performance comparison between NVIDIA GPUs and Intel processors, emphasizing the superior performance of GPUs. It also discusses the hardware architecture of GPUs, the advantages of GPU-accelerated computing, and the challenges of GPU programming. The lecture concludes by highlighting the importance of data-parallel computing and the CUDA programming model.