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Graphics Processing Unit (GPU)
Return to General-purpose computing on graphics processing units (GPGPU), CUDA
TLDR: GPUs (Graphics Processing Units) are specialized hardware accelerators originally designed for rendering graphics and rendering images, now widely used for computationally intensive tasks like machine learning and scientific simulations. Introduced in the late 1990s with products like the NVIDIA GeForce series, modern GPUs excel at GPU parallel processing, handling thousands of simultaneous operations, making them essential for applications requiring GPU high throughput and GPU efficiency.
https://en.wikipedia.org/wiki/Graphics_processing_unit
GPU architectures are optimized for handling parallel processing tasks, with thousands of GPU cores designed to execute simple operations across large datasets. In addition to graphics rendering, GPUs power fields like 3D rendering, video editing, and cryptocurrency mining. Modern GPUs include features like ray tracing, supported by RT Cores, and AI-driven technologies like Tensor Cores for tasks such as Deep Learning Super Sampling (DLSS). Frameworks like CUDA and OpenCL extend their utility to developers, enabling programming for a wide variety of computational workloads.
https://developer.nvidia.com/cuda-zone
GPUs are available as both consumer GPUs and professional GPUs, catering to gamers, creators, and researchers. Products like NVIDIA RTX and AMD Radeon cards offer high-performance options for gaming PCs, while workstation GPUs like NVIDIA Quadro and AMD Radeon Pro support specialized applications. The evolution of GPUs continues to push the boundaries of computational power, making them indispensable tools in modern computing environments.
https://www.amd.com/en/graphics
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- Snippet from Wikipedia: Graphics processing unit
A graphics processing unit (GPU) is a specialized electronic circuit designed for digital image processing and to accelerate computer graphics, being present either as a discrete video card or embedded on motherboards, mobile phones, personal computers, workstations, and game consoles. GPUs were later found to be useful for non-graphic calculations involving embarrassingly parallel problems due to their parallel structure. The ability of GPUs to rapidly perform vast numbers of calculations has led to their adoption in diverse fields including artificial intelligence (AI) where they excel at handling data-intensive and computationally demanding tasks. Other non-graphical uses include the training of neural networks and cryptocurrency mining.
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