With the CUDA Toolkit from NVIDIA, you can accelerate your C or C++ code by moving the computationally intensive portions of your code to an NVIDIA GPU.  In addition to providing drop-in library acceleration, you are able to efficiently access the massive parallel power of a GPU with a few new syntactic elements and calling functions from the CUDA Runtime API.

The CUDA Toolkit from NVIDIA is free and includes:

  • Visual and command-line debugger
  • Visual and command-line GPU profiler
  • Many GPU optimized libraries
  • The CUDA C/C++ compiler
  • GPU management tools
  • Lots of other features

Getting Started:

  1. Make sure you have an understanding of what CUDA is.
    • Read through the Introduction to CUDA C/C++ series on Mark Harris’ Parallel Forall blog.
  2. Try CUDA by taking a self-paced lab on nvidia.qwiklab.com. These labs only require a supported web browser and a network that allows Web Sockets. Click here to verify that your network & system support Web Sockets in section "Web Sockets (Port 80)", all check marks should be green.
  3. Download and install the CUDA Toolkit.
    • You can watch a quick how-to video for Windows showing this process:

    • Also see Getting Started Guides for Windows, Mac, and Linux.
  4. See how to quickly write your first CUDA C program by watching the following video:

Learning CUDA:

  1. Take the easily digestible, high-quality, and free Udacity Intro to Parallel Programming course which uses CUDA as the parallel programming platform of choice.
  2. Visit docs.nvidia.com for CUDA C/C++ documentation.
  3. Work through hands-on examples:
  4. Look through the code samples that come installed with the CUDA Toolkit.
  5. If you are working in C++, you should definitely check out the Thrust parallel template library.
  6. Browse and ask questions on stackoverflow.com or NVIDIA’s DevTalk forum.
  7. Learn more by:
  8. Look at the following for more advanced hands-on examples:

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Availability

The CUDA Toolkit is a free download from NVIDIA and is supported on Windows, Mac, and most standard Linux distributions.

  • Starting with CUDA 5.5, CUDA also supports the ARM architecture
  • For the host-side code in your application, the nvcc compiler will use your default host compiler.