GPU Architecture
GPU Architecture refers to the design and organization of Graphics Processing Units (GPUs), specialized hardware optimized for parallel processing of graphics and computational tasks. It encompasses components like streaming multiprocessors, memory hierarchies, and instruction sets that enable high-throughput data processing. Understanding GPU architecture is crucial for developing efficient applications in graphics rendering, scientific computing, and machine learning.
Developers should learn GPU architecture when working on performance-critical applications such as real-time graphics (e.g., video games, VR), high-performance computing (HPC) simulations, or AI/ML model training, as it allows for optimized code that leverages parallel processing capabilities. It is essential for roles involving CUDA, OpenCL, or Vulkan programming, where knowledge of memory management, thread scheduling, and hardware constraints directly impacts efficiency and speed.