GPU Processing
GPU processing refers to the use of Graphics Processing Units (GPUs) for general-purpose computing tasks beyond graphics rendering, leveraging their massively parallel architecture to accelerate computationally intensive workloads. It involves techniques like GPGPU (General-Purpose computing on GPUs) and frameworks such as CUDA or OpenCL to offload processing from CPUs to GPUs. This approach is particularly effective for tasks that can be broken down into many independent, parallel operations.
Developers should learn GPU processing when working on applications requiring high-performance computing, such as machine learning model training, scientific simulations, video processing, or real-time data analysis, where parallelizable algorithms can achieve significant speedups. It's essential for roles in AI/ML engineering, game development, financial modeling, and computational research to optimize performance and reduce processing times compared to CPU-only implementations.