GPU Parallelism
GPU parallelism is a computing paradigm that leverages the massively parallel architecture of Graphics Processing Units (GPUs) to accelerate data-intensive tasks beyond the capabilities of traditional CPUs. It involves executing many threads simultaneously across thousands of cores, making it ideal for workloads with high data parallelism, such as matrix operations, image processing, and scientific simulations. This approach is central to fields like machine learning, high-performance computing (HPC), and real-time graphics rendering.
Developers should learn GPU parallelism when working on applications that require intensive numerical computations or large-scale data processing, as it can provide orders-of-magnitude speedups compared to CPU-based implementations. Key use cases include training deep learning models with frameworks like TensorFlow or PyTorch, running complex simulations in physics or finance, and developing video games or VR applications with real-time graphics. It's essential for optimizing performance in domains where latency and throughput are critical.