Simulation / Modeling / Design

CUDACasts Episode 21: Porting a simple OpenCV sample to the Jetson TK1 GPU

Oct 13, 2014

By Mark Ebersole

In the previous?CUDACasts?episode, we saw how to flash your Jetson TK1 to the latest release of Linux4Tegra, and install both the CUDA toolkit and OpenCV SDK. ?We’ll continue exploring the power efficiency the Jetson TK1 Kepler-based GPU brings to computer vision by porting a simple OpenCV sample to run on the GPU. ?We’ll explore computer vision further in a future CUDACast when we look at the VisionWorks toolkit from NVIDIA.

CUDACasts are?short how-to screencast videos about new features and techniques for GPU programming. Click here for all CUDACasts.

To suggest a topic for a future episode of CUDACasts, or if you have any other feedback, please leave a comment below.

Related resources

DLI course: Building Video AI Applications at the Edge on Jetson Nano
GTC session: How To Write A CUDA Program: The Parallel Programming Edition
GTC session: Build CUDA Software at the Speed of Light
GTC session: The CUDA Python Developer’s Toolbox
NGC Containers: DeepStream
NGC Containers: Zero Shot Inference Service (Jetson)

Discuss (2)

About the Authors

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

View all posts by Mark Ebersole