Pi boards and V-GPU server are connected to the same switch, and since Pi has only 10/100M Ethernet connectivity, the performance is quite limited, especially when there’s a large amount of data transfer between host and virtual GPU. Nevertheless, it works with other ARM board with Gigabit (or even 10G) Ethernet as well, and we use Pi just because it’s cheap and easy to get.
You can run CUDA SDK sample applications (except those use OpenGL API) without any modification to source code. Every CUDA application runs transparently on virutal GPU(s).
What’s the application? Honestly, we don’t know the best use of running CUDA on Raspberry Pi, but I can list a few here if you find it interested. You may use it to build a classroom for CUDA training without buying GPU card for everyone. You may use it to build a GPU-accelerated Raspberry Pi farm as a flexible, yet powerful cluster. You may use it in your home automation powered by low-cost Pi+Arduino devices and process images using CUDA-accelerated OpenCV in the same process. You may find some other idea on the CARMA forum as well, and if you have any cool idea, let us know (in the comment or email) and we can provide you the V-GPU binary
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