It's time for me to give back to the Neuro-Evolution for Augmenting Topologies (NEAT) community. I am starting to port strict HyperNEAT to a stand-alone CUDA implementation. For those who don't know, CUDA is NVidia's Compute Unified Device Architecture.
Unless I hear otherwise, it will be a simple implementation for 1 multi-core CPU (of say 2 on-die cores) and 1 CUDA capable GPU (say a GTX 8800 or a GTX 295). Nothing too fancy, just enough to give insight into how I would implement HyperNEAT on CUDA. The way I'm currently doing this may be novel - I'm using a factory class to generate the actual CUDA code, and I probably should offer my experiment development GUI to help others understand this "magic smoke" a little better.
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