General-purpose computing on graphics processing units
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). The use of multiple video cards in one computer, or large numbers of graphics chips, further parallelizes the already parallel nature of graphics processing.
General-purpose computing on graphics processing units - Wikipedia Jump to content From Wikipedia, the free encyclopedia Use of a GPU for computations typically assigned to CPUs This article needs additional citations for verification . Please help improve this article by adding citations to reliable sources . Unsourced material may be challenged and removed. Find sources:   "General-purpose computing on graphics processing units"  –  news   · newspapers   · books   · scholar   · JSTOR ( February 2022 ) ( Learn how and when to remove this message ) General-pu
Explore this link on the map →saved by
related reading
- The Best GPUs for Deep Learning in 2023 — An In-depth Analysistimdettmers.com
- CUDA C++ Programming Guide (Legacy) — CUDA C++ Programming Guidedocs.nvidia.com
- Inside NVIDIA GPUs: Anatomy of high performance matmul kernels - Aleksa Gordićaleksagordic.com
- How to Think About GPUs | How To Scale Your Modeljax-ml.github.io
- CUDA - Wikipediaen.wikipedia.org
- Demystifying GPU Compute Architectures - by Babbagethechipletter.substack.com
- [2410.05686] Deep Learning and Machine Learning with GPGPU and CUDA: Unlocking the Power of Parallel Computingarxiv.org
- Execution Model - SLING user documentationdoc.sling.si
- The Best GPUs for Deep Learning in 2023 — An In-depth Analysistimdettmers.com
- GPU Performance Background User's Guide - NVIDIA Docsdocs.nvidia.com
- TPU Deep Divehenryhmko.github.io
- Teaching GPU programming in p5.js: now with compute shaders - Dave Pagurekdavepagurek.com