GPU Accelerated Computing with Python

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python is one of the fastest growing and most popular programming languages available. However, as an interpreted language, it has been considered too slow for high-performance computing.  That has now changed with the release of the NumbaPro Python compiler from Continuum Analytics.

CUDA Python – Using the NumbaPro Python compiler, which is part of the Anaconda Accelerate package from Continuum Analytics, you get the best of both worlds: rapid iterative development and all other benefits of Python combined with the speed of a compiled language targeting both CPUs and NVIDIA GPUs.

Getting Started

  1. If you are new to Python, the website is an excellent source for getting started material.
  2. Read this blog post if you are unsure what CUDA or GPU Computing is all about.
  3. Try CUDA by taking a self-paced lab on 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.
  4. Watch the first CUDA Python CUDACast:


  5. Install Anaconda Accelerate
  6. First install the free Anaconda package from this location.
  7. Once Anaconda is installed, you can install a trial-version of the Accelerate package by using Anaconda’s package manager and running conda install accelerate.  See here for more detailed information.  Please note that the Anaconda Accelerate package is free for Academic use.

Learning CUDA

  1. For documentation, see the Continuum website for these various topics:
    • Learn more about libraries
    • See how to use vectorize to automatically accelerate functions
    • Writing CUDA directly in Python code
  2. Browse through the following code examples:

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