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.
Sign Up for the FREE parallel programming class at Udacity today!
Drop-in, Industry standard libraries replace MKL, IPP, FFTW and other widely used libraries. Some feature automatic multi-GPU scaling,
Easy: simply insert hints in your code
Open: run on either CPU or GPU
Powerful: tap into the power of GPUs within minutes
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.