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 running a job overnight and running a job for the entire weekend. Best, -Chaz On Tue, Mar 1, 2016 at 1:03 PM, Rémi 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? > > Rémi > > 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 >> _______________________________________________ >> Computer-go mailing list >> Computer-go@computer-go.org >> http://computer-go.org/mailman/listinfo/computer-go >> > > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go
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