[Computer-go] Zero performance

Gian-Carlo Pascutto gcp at sjeng.org
Fri Oct 20 10:44:39 PDT 2017

I reconstructed the full AlphaGo Zero network in Caffe:

I did some performance measurements, with what should be
state-of-the-art on consumer hardware:

GTX 1080 Ti
NVIDIA-Caffe + CUDA 9 + cuDNN 7
batch size = 8

Memory use is about ~2G. (It's much more for learning, the original
minibatch size of 32 wouldn't fit on this card!)

Running 2000 iterations takes 93 seconds.

In the AlphaGo paper, they claim 0.4 seconds to do 1600 MCTS
simulations, and they expand 1 node per visit (if I got it right) so
that would be 1600 network evaluations as well, or 200 of my iterations.

So it would take me ~9.3s to produce a self-play move, compared to 0.4s
for them.

I would like to extrapolate how long it will take to reproduce the
research, but I think I'm missing how many GPUs are in each self-play
worker (4 TPU or 64 GPU or ?), or perhaps the average length of the games.

Let's say the latter is around 200 moves. They generated 29 million
games for the final result, which means it's going to take me about 1700
years to replicate this. I initially estimated 7 years based on the
reported 64 GPU vs 1 GPU, but this seems far worse. Did I miss anything
in the calculations above, or was it really a *pile* of those 64 GPU

Because the performance on playing seems reasonable (you would be able
to actually run the MCTS on a consumer machine, and hence end up with a
strong program), I would be interested in setting up a distributed
effort for this. But realistically there will be maybe 10 people
joining, 80 if we're very lucky (looking at Stockfish numbers). That
means it'd still take 20 to 170 years.

Someone please tell me I missed a factor of 100 or more somewhere. I'd
love to be wrong here.


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