GPU Accelerated Computing
GPU Accelerated Computing is a computing paradigm that leverages the parallel processing capabilities of Graphics Processing Units (GPUs) to perform general-purpose computations beyond traditional graphics rendering. It involves offloading computationally intensive tasks from the Central Processing Unit (CPU) to the GPU, which excels at handling thousands of concurrent threads. This approach is widely used in fields like machine learning, scientific simulations, and data analytics to achieve significant performance gains.
Developers should learn GPU Accelerated Computing when working on applications that require high-performance parallel processing, such as training deep learning models, running complex simulations, or processing large datasets. It is essential for optimizing performance in domains like artificial intelligence, high-performance computing (HPC), and real-time data processing, where CPU-based solutions may be too slow or inefficient.