GPU Acceleration
TLDR: GPU acceleration refers to the use of Graphics Processing Units (GPUs) to perform computational tasks that would typically be handled by a CPU. By leveraging the massive parallel processing capabilities of GPUs, applications can execute complex calculations and data-intensive workloads significantly faster. GPU acceleration is widely used in fields like machine learning, 3D rendering, and scientific simulations, making it an essential component of modern computing.
https://en.wikipedia.org/wiki/GPU_computing
The core advantage of GPU acceleration lies in its ability to process thousands of threads simultaneously, making it ideal for tasks like matrix multiplications and vector calculations. Frameworks like CUDA and OpenCL enable developers to harness GPU power for general-purpose computing, beyond traditional graphics rendering. NVIDIA GPUs with Tensor Cores excel in AI workloads, while AMD GPUs optimized with RDNA architecture deliver exceptional performance in gaming and content creation.
https://developer.nvidia.com/cuda-zone
GPU acceleration has revolutionized various industries by enabling real-time processing and analysis of massive datasets. In gaming, it powers technologies like ray tracing and DLSS, creating lifelike visuals. In machine learning, GPU-accelerated frameworks like TensorFlow allow for faster training of neural networks. As advancements in GPU architectures continue, GPU acceleration remains at the forefront of innovation in computational performance and efficiency.