These keywords let the developer express massive amounts of parallelism and direct the compiler (or interpreter) to those portions of the application on GPU accelerators. With CUDA, developers write programs using an ever-expanding list of supported languages that includes C, C++, Fortran, Python and MATLAB, and incorporate extensions to these languages in the form of a few basic keywords.

From L to R, top to bottom: The NVIDIA Ampere GPU, MIG, Tensor Cores, RT Cores, structural sparsity, and NVLink. The flexibility and programmability of CUDA have made it the platform of choice for researching and deploying new deep learning and parallel computing algorithms.ĬUDA also makes it easy for developers to take advantage of all the latest GPU architecture innovations - as found in our most recent NVIDIA Ampere GPU architecture. Thousands of GPU-accelerated applications are built on the NVIDIA CUDA parallel computing platform. With over 150 CUDA-based libraries, SDKs, and profiling and optimization tools, it represents far more than that. Some people confuse CUDA, launched in 2006, for a programming language - or maybe an API. Both brick-and-mortar and online stores use CUDA to analyze customer purchases and buyer data to make recommendations and place ads. Cars use CUDA to augment autonomous driving. In addition to accelerating high performance computing (HPC) and research applications, CUDA has also been widely adopted across consumer and industrial ecosystems.įor example, pharmaceutical companies use CUDA to discover promising new treatments. With more than 20 million downloads to date, CUDA helps developers speed up their applications by harnessing the power of GPU accelerators. CUDA is a parallel computing platform and programming model created by NVIDIA.

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