Get Started - Parallel Computing

Learn about options for deploying CUDA and GPU Acceleration in your project

Get started quickly with GPU Computing using the solution that best meets your needs. Your options include simply dropping in a GPU-accelerated library, adding a few GPU Directives in your code, or designing your own parallel algorithms - and you can combine these approaches to accelerate your applications.

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Optimized Libraries

Drop-in, Industry standard libraries replace MKL, IPP, FFTW and other widely used libraries. Some feature automatic multi-GPU scaling,

Get Started with GPU-Accelerated Libraries

Compiler Directives

Easy: simply insert hints in your code
Open: run on either CPU or GPU
Powerful: tap into the power of GPUs within minutes

Get Started with Directives

Programming Language

Develop your own parallel applications and libraries using a programming language you already know.

Get Started With:

Looking for more? Learn more about GPU-accelerated applications, tools and libraries

CUDA capable Notebook and Desktop PCs

All the latest NVIDIA GeForce, Quadro and TESLA GPU's are CUDA Capable. If you are looking to buy a powerful  PC with one of the latest NVIDIA GPUs you will find many listed on the geforce.com website, there are many power notebook PCs featuring GPUs with the latest compute architectures.