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Thursday, August 15 • 10:30am - 12:30pm
Intro to GPU Computing with Python

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Audience: A little bit of Python experience is helpful, no GPU Computing experience required.


Until recently, the ability to directly program for an NVIDIA GPU has been restricted to languages such as C/C++ or Fortran.  With the release of Anaconda Accelerate from Continuum Analystics, is now possible to write GPU code directly in Python and have it execute natively on an NVIDIA GPU.

This tutorial will briefly cover an introduction to GPU programming, after which we will move to hands-on exercises using Python, hosted on systems in the cloud with Amazon’s AWS.  The exercises will cover concepts such as memory management, easily launching kernels with thousands of threads, and profiling the created GPU code.  We will also look at simpler methods to accelerate Python functions using Accelerate’s built-in @vectorize decorator.  This is a “bring your own computer” hands-on tutorial.


Mark Ebersole

As CUDA Educator at NVIDIA, Mark Ebersole teaches developers the benefits of GPU computing using the NVIDIA CUDA parallel computing platform and programming model, and the benefits of GPU computing. With more than ten years of experience as a systems programmer, Mark has spent much of his time at NVIDIA as a GPU systems diagnostics programmer in which he developed a tool to test, debug, validate, and verify GPUs from pre-emulation through bringup... Read More →

Thursday August 15, 2013 10:30am - 12:30pm
College of Business Room 8

Attendees (2)