OpenCV Programming with CUDA on Linux - 3: Running your First CUDA Program

In this tutorial I show how to run your first CUDA program running on a CUDA-enabled graphics processing unit from NVIDIA.

NVIDIA GPUs are built on what’s known as the CUDA Architecture. You can think of the CUDA Architecture as the scheme by which NVIDIA has built GPUs that can perform both traditional graphics rendering tasks and general-purpose tasks. To program CUDA GPUs, the language that is used is CUDA C. CUDA C is essentially C with a handful of extensions to allow programming of massively parallel machines like NVIDIA GPUs.

OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code.
The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc. OpenCV has more than 47 thousand people in their user community and an estimated number of downloads exceeding 7 million. The library is used extensively in companies, research groups and by governmental bodies.

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