Re: [Computer-go] Using GPUs?

[Steven Clark]

Here are the papers I was thinking of:

http://arxiv.org/abs/1412.6564 (nvidia gtx titan black)
http://arxiv.org/abs/1412.3409 (nvidia gtx 780)

On Fri, Jun 26, 2015 at 2:09 AM, Nikos Papachristou 
wrote:

> Not go related, but you may find this deep learning GPU hardware guide
> useful:
> https://timdettmers.wordpress.com/2015/03/09/deep-learning-hardware-guide/
>
> As for hardware breakthroughs, Nvidia has announced that its next
> generation GPUs (codenamed Pascal) will offer 10x the performance in 2016,
> so you might want to wait a little more.
>
> Nikos
>
> On Thu, Jun 25, 2015 at 8:18 PM, Darren Cook  wrote:
>
>> I wondered if any of the current go programs are using GPUs.
>>
>> If yes, what is good to look for in a GPU? Links to essential reading on
>> this topic would be welcome. (*)
>>
>> If not, is there some hardware breakthrough being waited for, or some
>> algorithmic one?
>>
>> Darren
>>
>> *: After many years of being happy with built-in graphics, I'm now
>> thinking to get a "gaming" PC, to show off some WebGL data
>> visualizations. Assuming the cost is in the same ballpark, I thought I'd
>> get one that would allow some scientific computing experiments too.
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[Computer-go] NNGS go server

[Detlef Schmicker]

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Is there a client to watch the games on NNGS go servers?


Thanks a lot

Detlef
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Re: [Computer-go] Using GPUs?

[Darren Cook]

Steven wrote:
> http://arxiv.org/abs/1412.6564 (nvidia gtx titan black)
> http://arxiv.org/abs/1412.3409 (nvidia gtx 780)

Thanks - I had read those papers but hadn't realized the neural nets
were run on GPUs.

Nikos wrote:
>> https://timdettmers.wordpress.com/2015/03/09/deep-learning-hardware-guide/

This was very useful, thanks!

>> As for hardware breakthroughs, Nvidia has announced that its next
>> generation GPUs (codenamed Pascal) will offer 10x the performance in 2016,
>> so you might want to wait a little more.

One of the comments, on the above blog, questions that 10x speed-up:

https://timdettmers.wordpress.com/2015/03/09/deep-learning-hardware-guide/comment-page-1/#comment-336

Confirmed here:
  http://blogs.nvidia.com/blog/2015/03/17/pascal/

So, currently they use a 32-bit float, rather than a 64-bit double, but
will reduce that to 16-bit to get a double speed-up. Assuming they've
been listening to customers properly, that must mean 16-bit floats are
good enough for neural nets?

Darren

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Re: [Computer-go] NNGS go server

[Hideki Kato]

Jago can. http://www.rene-grothmann.de/jago/

Hideki

Detlef Schmicker: <558d5159.1000...@physik.de>:
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>Is there a client to watch the games on NNGS go servers?
>
>
>Thanks a lot
>
>Detlef
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Hideki Kato 
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Re: [Computer-go] Using GPUs?

[Darren Cook]

> It is not exactly Go, but i have a monte-carlo tree searcher on the GPU for
> the game of Hex 8x8
> Here is a github link https://github.com/dshawul/GpuHex

The engine looks to be just the middle 450 lines of code; quite compact!
So running playouts on a GPU worked out well?

Would doing the same thing for go be just a matter of writing more lines
of code, or needing more memory on the GPU, or is there some more
fundamental difference between hex and go that makes the latter less
suitable? (e.g. in hex pieces are only added to the board, whereas in go
they can be removed and loops can happen - does that make GPU-ing
algorithms harder?)

Darren

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Re: [Computer-go] Using GPUs?

[Daniel Shawul]

Yes it performed well for Hex8x8; i got a speed up of 60x compared to CPU
when i tested it about 2 years ago on a not-so-modern GPU (128 cores IIRC).
However, the playouts in Hex are much simpler than that of Go.
For instance, I check for termination of game once when the board is
completely full, i.e after all 64
stones are placed. This has allowed me to do only with two bitboards:
empty_squares and white_stones_squares.
It is weird that you don't even need to store black_stones. Also there are
no captures to complicate matters.

I have a "chess" branch in the github repo that I experimented on, but it
didn't work out well. First, chess is not suitable for monte-carlo search.
Second, board representation requires more register/shared memory so it is
difficult to make one thread do one playout by itself. Right now a warp (32
threads) get the same position from the MCTS tree, then each do their own
playouts. There won't be lots of divergence as all they do is place a stone
until board is completely full. I guess the memory limitation issue affects
Go as well. GPU for Go is definitely harder than Hex8x8, which I handpicked
for better performance. But I believe one should be able to get a good Go
or Checkers engine using MCTS on the GPU.
Daniel


On Fri, Jun 26, 2015 at 6:29 AM, Darren Cook  wrote:

> > It is not exactly Go, but i have a monte-carlo tree searcher on the GPU
> for
> > the game of Hex 8x8
> > Here is a github link https://github.com/dshawul/GpuHex
>
> The engine looks to be just the middle 450 lines of code; quite compact!
> So running playouts on a GPU worked out well?
>
> Would doing the same thing for go be just a matter of writing more lines
> of code, or needing more memory on the GPU, or is there some more
> fundamental difference between hex and go that makes the latter less
> suitable? (e.g. in hex pieces are only added to the board, whereas in go
> they can be removed and loops can happen - does that make GPU-ing
> algorithms harder?)
>
> Darren
>
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