On Sun, Mar 06, 2016 at 12:40:11PM +0100, Rémi Coulom wrote:
> I checked again, and it is using only one single thread for everything in
> CPU mode. I simply use caffe::Caffe::set_mode(caffe::Caffe::CPU), and it is
> using only one core (I checked with top in a long loop of calls to the
> network).
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
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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 recently
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 CP
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 believe
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 W
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 runni
