CUDA™ is a parallel computing platform and programming model that enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU).
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 300 million CUDA-enabled GPUs in notebooks, workstations, compute clusters and supercomputers. Learn more about GPU-accelerated applications available for astronomy, biology, chemistry, physics, data mining, manufacturing, finance, and more on the software solutions page.
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.