About CUDA – More Than A Programming Model

Published February 23, 2016

The CUDA compute platform extends from the 1000s of general purpose compute processors featured in our GPU’s compute architecture, parallel computing extensions to many popular languages, powerful drop-in accelerated libraries to turn key applications and cloud based compute appliances. CUDA extends beyond the popular CUDA Toolkit and the CUDA C/C++ programming language, we invite you to explore the CUDA Ecosystem and learn how you can accelerate your applications.

Widely Used By Researchers

Since its introduction in 2006, CUDA has been widely deployed through thousands of applications and published research papers, and supported by an installed base of over 500 million CUDA-enabled GPUs in notebooks, workstations, compute clusters and supercomputers.

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Real People – Real Success Stories

Many researchers and developers have used the CUDA Platform to push the state of the art of their work, read some of their stories in the CUDA In Action Spotlight Series.

Acceleration For All Domains

Learn more about GPU-accelerated applications available for astronomy, biology, chemistry, physics, data mining, manufacturing, finance, and more on the software solutions page and industry solutions page. Check out our dedicated Geo-Intelligence for Developers page. Read some real industrial application case studies.

How to get started

Software developers, scientists and researchers can add support for GPU acceleration in their own applications using one of three simple approaches:

Drop in a GPU-accelerated library to replace or augment CPU-only libraries such as MKL BLAS, IPP, FFTW and other widely-used libraries
Automatically parallelize loops in Fortran or C code using OpenACC directives for accelerators
Develop custom parallel algorithms and libraries using a familiar programming language such as C, C++, C#, Fortran, Java, Python, etc.

Start accelerating your application today, learn how by visiting the Getting Started Page.

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Posted in Learning, News | Tagged CUDA, Deep Learning, Nvidia, Programming

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