Check out:

If you find any issues please file a bug (requires membership of the CUDA Registered Developer Program).

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!

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

The OpenACC Toolkit from NVIDIA offers scientists and researchers a simple way to accelerated scientific computing without significant programming effort. Simply insert hints (or “directives”) in C or Fortran code and the OpenACC compiler runs the code on the GPU.

  • Simple: Insert compiler hints to instantly tap into thousands of computational cores in the GPU
  • Powerful: Delivers up to 10x faster application performance
  • Free: The OpenACC Toolkit with compiler included is available at no charge for academia*

Get Your Free OpenACC Toolkit Now

DOWNLOAD

Application Acceleration with OpenACC on GPUs


LS-DALTON: Benchmark on Oak Ridge Titan Supercomputer, AMD CPU vs Tesla K20X GPU. Test input: Alanine-3 on CCSD(T) module. Additional information: NICAM COSMO




"OpenACC makes GPU computing approachable for domain scientists. Initial OpenACC implementation required only minor effort, and more importantly,no modifications of our existing CPU implementation"

GPUs are accelerating many applications across numerous industries. 

Learn more

Applications with high arithmetic density can enjoy amazing GPU acceleration.

Learn more

Adding acceleration to your application can be as easy as calling a library function.

Learn more

NVIDIA GPUs power millions of desktops, notebooks, workstations and supercomputers around the world, accelerating computationally-intensive tasks for consumers, professionals, scientists, and researchers.

Find out all about CUDA and GPU Computing by attending our GPU Computing Webinars and joining our free-to-join CUDA Registered developer Program.

  • Learn about Tesla for technical and scientific computing
  • Learn about Quadro for professional visualization

If you have an older NVIDIA GPU you may find it listed on our legacy CUDA GPUs page
Click the sections below to expand

Sections

General Questions

Q: What is CUDA?

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 hundreds of millions of CUDA-enabled GPUs in notebooks, workstations, compute clusters and supercomputers.  Applications used in astronomy, biology, chemistry, physics, data mining, manufacturing, finance, and other computationally intense fields are increasing using CUDA to deliver the benefits of GPU acceleration.
 

Q: What is NVIDIA Tesla™?

With the world’s first teraflop many-core processor, NVIDIA® Tesla™ computing solutions enable the necessary transition to energy efficient parallel computing power. With thousands of CUDA cores per processor , Tesla scales to solve the world’s most important computing challenges—quickly and accurately.

Q: What is OpenACC?