CUDA 7 Downloads
Join us for these two live webinars:
- CUDA 7 Features and Overview, 10am (PDT), Friday, April 10th
- CUDA 7 Performance Overview, 11:30am (PDT), Wednesday, April 15th
Check out The Power of C++11 in CUDA 7, another technical blog on Parallel Forall.
Please Note: In CUDA 7.0, the cuFFT library has a known issue that can lead to incorrect results for certain inputs sizes less than or equal to 1920 in any dimension when cufftSetStream() is passed a non-blocking stream (e.g., one created using the cudaStreamNonBlocking flag of the CUDA Runtime API or the CU_STREAM_NON_BLOCKING flag of the CUDA Driver API).
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
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
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?