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CUDACasts Episode #10: Accelerate Python on GPUs

This week’s CUDACast continues the Parallel Forall Python theme kicked off in last week’s post by Mark Harris, demonstrating exciting new support for CUDA acceleration in Python with NumbaPro. This video is the first in a 3-part series showing various ways to accelerate your Python code on NVIDIA GPUs.

Tomorrow you won’t want to miss the chance to learn about Python GPU acceleration with NumbaPro from its creators, in a GTC Express Webinar called “Pythonic Parallel Patterns for the GPU with NumbaPro” from Siu Kwan Lam, NumbaPro’s primary author at Continuum Analytics. Click the link to sign up now!

To request a topic for a future episode of CUDACasts, or if you have any other feedback, please use the contact form or leave a comment to let us know.

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About Mark Ebersole

As CUDA Educator at NVIDIA, Mark Ebersole teaches developers and programmers about the NVIDIA CUDA parallel computing platform and programming model, and the benefits of GPU computing. With more than ten years of experience as a low-level 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 and into production. Before joining NVIDIA, he worked for IBM developing Linux drivers for the IBM iSeries server. Mark holds a BS degree in math and computer science from St. Cloud State University. Follow @cudahamster on Twitter