GPU Programming
GPU programming involves writing code to leverage the parallel processing capabilities of Graphics Processing Units (GPUs) for general-purpose computing tasks beyond graphics rendering. It enables high-performance computation by executing thousands of threads simultaneously, making it ideal for data-intensive applications like machine learning, scientific simulations, and real-time processing. This is typically done using frameworks like CUDA, OpenCL, or Vulkan to interface with GPU hardware.
Developers should learn GPU programming when working on computationally intensive tasks that benefit from massive parallelism, such as training deep learning models, processing large datasets, or running complex simulations in fields like physics or finance. It is essential for optimizing performance in applications where CPU-based processing becomes a bottleneck, such as real-time video analysis, cryptocurrency mining, or high-frequency trading systems.