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General-purpose computing on graphics processing units (GPGPU, or less often GPGP) is the use of a graphics processing unit (GPU), which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the central processing unit (CPU).
Advances in GPU technology in cars helped advance self-driving technology. [40] AMD's Radeon HD 6000 series cards were released in 2010, and in 2011 AMD released its 6000M Series discrete GPUs for mobile devices. [41] The Kepler line of graphics cards by Nvidia were released in 2012 and were used in the Nvidia's 600 and 700 series cards.
CUDA is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements for the execution of compute kernels. [6] In addition to drivers and runtime kernels, the CUDA platform includes compilers, libraries and developer tools to help programmers accelerate their applications.
ROCm [3] is an Advanced Micro Devices (AMD) software stack for graphics processing unit (GPU) programming. ROCm spans several domains: general-purpose computing on graphics processing units (GPGPU), high performance computing (HPC), heterogeneous computing.
A GPU cluster is a computer cluster in which each node is equipped with a graphics processing unit (GPU). By harnessing the computational power of modern GPUs via general-purpose computing on graphics processing units (GPGPU), very fast calculations can be performed with a GPU cluster. Titan, the first supercomputer to use GPUs
OpenCL (Open Computing Language) is a framework for writing programs that execute across heterogeneous platforms consisting of central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs) and other processors or hardware accelerators.
The book Programming Massively Parallel Processors by Kirk and Hwu suggests that GPU computing is the general term and GPGPU refers to a special type. I preferred the non-jargonistic "GPU computing" to the acronym GPGPU. I kind of wish I had moved the article to "GPU computing" rather than creating a redirect from "GPU computing".
GPU virtualization refers to technologies that allow the use of a GPU to accelerate graphics or GPGPU applications running on a virtual machine. GPU virtualization is used in various applications such as desktop virtualization , [ 1 ] cloud gaming [ 2 ] and computational science (e.g. hydrodynamics simulations).