<a href="http://blog.accelereyes.com/blog/wp-content/uploads/2011/09/OpenCV_Logo.png"><img class="alignright size-full wp-image-1777" title="OpenCV_Logo" src="http://blog.accelereyes.com/blog/wp-content/uploads/2011/09/OpenCV_Logo.png" alt="" width="239" height="295" /></a><a title="OpenCV" href="http://opencv.willowgarage.com/wiki/">OpenCV</a> is one of the most popular computer vision toolkits, and over the last year they've been integrating more <a href="http://opencv.willowgarage.com/wiki/OpenCV_GPU">GPU processing</a> into the core.

One of the most common image processing tasks is convolution. Since LibJacket and <a href="http://opencv.itseez.com/modules/imgproc/doc/filtering.html">OpenCV</a> both support this, one of my coworkers rolled up his sleeves and benchmarked the latest versions from both libraries: OpenCV/CPU, OpenCV/GPU, LibJacket.

Jump over to his personal website for the <a href="http://mcclanahoochie.com/blog/2011/09/opencv-vs-libjacket-gpu-sobel-filtering/">full benchmark results and source code</a>.  From the graphs, the GPU implementations from OpenCV and LibJacket both easily outperform the default CPU version in OpenCV, but notice that LibJacket pushes performance even further and dominates OpenCV's GPU implementation, especially when using separable filters.

We've worked really hard the last few years to produce a reliable, high-performance commercial library and run-time.  If you're interested in the best software for GPU computing, you might consider buying a license to save yourself time and to boost the end-to-end performance of your code.

To learn more about LibJacket, <a href="http://accelereyes.com/libjacket_tour">take the tour</a>.

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Related: <a title="Image Processing with ArrayFire and OpenCV on the GPU" href="http://blog.accelereyes.com/blog/2012/09/19/image-processing-with-arrayfire-and-opencv/">ArrayFire+OpenCV.</a>