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).
Rémi
On 03/03/2016 09:19 AM, David Fotland wrote:
If you are using caffe, the network evaluator is single threaded, but it spends
almost all of its time in BLAS, which uses one thread per virtual CPU. On a
somewhat slower i7, I’m seeing about 200 ms.
David
-----Original Message-----
From: Computer-go [mailto:computer-go-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 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 it is still possible to have a very
strong CPU-based program.
Or is it possible to evaluate faster on the GPU by using a batch?
R mi
On 03/02/2016 09:43 AM, Petr Baudis wrote:
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 Wed, Mar 02, 2016 at 12:29:41AM -0800, Chaz G. wrote:
R mi,
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 running a job
overnight and running a job for the entire weekend.
Best,
-Chaz
On Tue, Mar 1, 2016 at 1:03 PM, R mi Coulom <remi.cou...@free.fr>
wrote:
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= 260 sec / memory = 0.8 GB
* CPU: time= 752 sec / memory = 3.5 GiB //Memory data is from
system
monitor.
"
This does not look so tremendous to me. What kind of speed
difference do you get for Go networks?
R mi
On 03/01/2016 06:19 PM, Petr Baudis wrote:
On Tue, Mar 01, 2016 at 09:14:39AM -0800, David Fotland wrote:
Very interesting, but it should also mention Aya.
I'm working on this as well, but I haven t bought any hardware
yet. My goal is not to get 7 dan on expensive hardware, but to
get as much strength as I can on standard PC hardware. I'll be
looking at much smaller nets, that don t need a GPU to run. I'll
have to buy a GPU for training.
But I think most people who play Go are also fans of computer games
that often do use GPUs. :-) Of course, it's something totally
different from NVidia Keplers, but still the step up from a CPU is
tremendous.
Petr Baudis
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