> You keep on missing the point.

I think you misunderstand. I absolutely understand your point. I'm explicitly rejecting your point. There's a crucial difference.

The evidence laid out in this test does not necessarily lead to the conclusion you have here. There are a number of confounding factors, that if I had more time... I'd like to investigate.

True, your point is ONE possible story for what is going on here. But alas, my instincts suggest something else is going on. I think CNNs have simply been extremely well optimized for the GPU platform, and that indeed, they are one of the few algorithms that run extremely well on a GPU.

I'm curious how a well-put together "classical" chess AI would work if it were ported to a GPU. I understand that no such chess AI has ever been written, but that doesn't change my curiosity.

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EDIT:

> Chess is not strictly about computing power, and neural-network evaluation functions are vastly better.

I just thought of a way that would test this assertion. Instead of porting Stockfish to a GPU, port LeelaZero to a CPU. Run the Neural Net on the same set of hardware, and see who wins.

That way, Stockfish keeps its (cannot be scaled) centralized hash table / lazy SMP algorithm, while LeelaZero runs at the same compute-power that Stockfish has.