Ben Goertzel writes:> http://www.nvidia.com/page/home.html> > Anyone know what are the weaknesses of these GPU's as opposed to> ordinary processors?> > They are good at linear algebra and number crunching, obviously.> > Is there some reason they would be bad at, say, MOSES learning? These parallel hardware innovations are indeed very exciting. I recently purchased a PC with two of these GPUs in it to play with. Like JoSH, I think that "number crunching" is The Way To Go. Unfortunately, these will be spectacularly bad at evaluating individuals for genetic programming. First, although they can do standard logic, program flow, and integer operations, that doesn't make very good use of the transistor count since the bulk of the silicon is dedicated to floating point arithmetic. Second, and more important, the programming model is SIMD, which means that the processors have to be running the same program. If, for example, and "if" statement's condition is satisfied on one processor but not the others, the others have to wait for the code inside to finish so they can all synchronize again. That would be terrible for evaluating heterogenous program trees. You're going to get your speedup over the coming years on that task from multicore CPUS that can run heterogenous threads. However, intuitively I think this massively parallel SIMD type of hardware might work rather well for propagation through your Probabilistic Logic Networks, depending on the details.
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