CUDA 7 Downloads
- CUDA 7 Performance Report and Webinar Recording
- An informative webinar by Ujval Kapasi, NVIDIA's CUDA Product Manager CUDA 7 Features and Overview
- The Power of C++11 in CUDA 7, another technical blog on Parallel Forall.
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
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
Application Acceleration with OpenACC on GPUs
"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"
NVIDIA GPUs power millions of desktops, notebooks, workstations and supercomputers around the world, accelerating computationally-intensive tasks for consumers, professionals, scientists, and researchers.
- 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
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?