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== History and Current State of CUDA ==
[[File:Arch_NVIDIA.jpg|right]]
NVIDIA’s first CUDA architecture was called Fermi.
= CUDA Application Domains =
[[File:CUDA_App_Domains.jpg|right]]
“CUDA and Nvidia GPUs have been adopted in many areas that need high floating-point computing performance, as summarized pictorially in the image above. A more comprehensive list includes:
1. Computational finance
2. Climate, weather, and ocean modeling
3. Data science and analytics
4. Deep learning and machine learning
5. Defense and intelligence
6. Manufacturing/AEC (Architecture, Engineering, and Construction): CAD and CAE (including computational fluid dynamics, computational structural mechanics, design and visualization, and electronic design automation)
7. Media and entertainment (including animation, modeling, and rendering; color correction and grain management; compositing; finishing and effects; editing; encoding and digital distribution; on-air graphics; on-set, review, and stereo tools; and weather graphics)
8. Medical imaging
9. Oil and gas
10. Research: Higher education and supercomputing (including computational chemistry and biology, numerical analytics, physics, and scientific visualization)
11. Safety and security
12. Tools and management”
[https://www.infoworld.com/article/3299703/what-is-cuda-parallel-programming-for-gpus.html Source]
Due to its proprietary nature, a limitation of CUDA is the number of devices supported and systems it can extend. This proprietary nature allows NVIDIA to completely maximize performance, resulting in CUDA outperforming OpenCL, at least on any given CUDA enabled device.
[[File:CUDA_pt_1.gif]]
[[File:CUDA_pt_2.gif]]