In this article, the author aims to provide a basic understanding of how GPUs work, particularly in comparison to CPUs. They highlight that CPUs are designed for sequential execution, with a focus on reducing instruction execution latency. In contrast, GPUs prioritize massive levels of parallelism and high throughput, making them ideal for applications such as video games, graphics, numerical computing, and deep learning. The article explains the key components of GPU hardware, including streaming multiprocessors, shared memory, caches, and global memory. It also delves into the execution of GPU kernels and the optimization of GPU resources for maximum occupancy. Overall, the article serves as a helpful introduction to GPU architecture and programming.
https://codeconfessions.substack.com/p/gpu-computing