] On Behalf
Of Rémi Coulom
Sent: Wednesday, March 02, 2016 1:23 AM
To: computer-go@computer-go.org
Subject: Re: [Computer-go] CPU vs GPU
I tried Detlef's 54% NN on my machine. CPU = i7-5930K, GPU = GTX 980
(not using cuDNN).
On the CPU, I get 176 ms time, and 10 ms on the GPU (IIRC, someone
reported 6 ms
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you can use caffe with time on the command line.
It gives you forward and backward time for a batch.
In my tests the batch size was not too important (I think, because the
net is quite large)...
cuDNN helps a lot in training, I did not test
o-boun...@computer-go.org] On Behalf
> Of Rémi Coulom
> Sent: Wednesday, March 02, 2016 1:23 AM
> To: computer-go@computer-go.org
> Subject: Re: [Computer-go] CPU vs GPU
>
> I tried Detlef's 54% NN on my machine. CPU = i7-5930K, GPU = GTX 980
> (not using cuDNN).
>
> On the
I tried Detlef's 54% NN on my machine. CPU = i7-5930K, GPU = GTX 980
(not using cuDNN).
On the CPU, I get 176 ms time, and 10 ms on the GPU (IIRC, someone
reported 6 ms with cuDNN). But it is using only one core on the CPU,
whereas it is using the full GPU.
If this is correct, then I
Also, reading more of that pull request, the guy benchmarking it had old
nvidia driver version which came with about 50% performance hit. So I'm
not sure what were the final numbers. (And whether current caffe
version can actually match these numbers, since this pull request wasn't
merged.)
On
Rémi,
Nvidia launched the K20 GPU in late 2012. Since then, GPUs and their
convolution algorithms have improved considerably, while CPU performance
has been relatively stagnant. I would expect about a 10x improvement with
2016 hardware.
When it comes to training, it's the difference between
How tremendous is it? On that page, I find this data:
https://github.com/BVLC/caffe/pull/439
"
These are setup details:
* Desktop: CPU i7-4770 (Haswell), 3.5 GHz , DRAM - 16 GB; GPU K20.
* Ubuntu 12.04; gcc 4.7.3; MKL 11.1.
Test:: imagenet, 100 train iteration (batch = 256).
* GPU: time=
