Re: [Computer-go] CPU vs GPU

2016-03-06 Thread Rémi Coulom 
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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Re: [Computer-go] CPU vs GPU

2016-03-03 Thread Detlef Schmicker 
-BEGIN PGP SIGNED MESSAGE-
Hash: SHA1

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 but it was 2
times faster end of last year and improved by a factor of 4 during
this year :)

Detlef

Am 02.03.2016 um 10:22 schrieb Rémi Coulom:
> 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?
> 
> Rémi
> 
> 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:
>>> 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 
>>>  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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>>> mailing list Computer-go@computer-go.org 
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> 
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Re: [Computer-go] CPU vs GPU

2016-03-03 Thread David Fotland 
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
> >>>> ___
> >>>> 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
> >> ___
> >> Computer-go mailing list
> >> Computer-go@computer-go.org
> >> http://computer-go.org/mailman/listinfo/computer-go
> >
> 
> ___
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Re: [Computer-go] CPU vs GPU

2016-03-02 Thread Rémi Coulom 
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?

Rémi

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:

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  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
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Re: [Computer-go] CPU vs GPU

2016-03-02 Thread Petr Baudis 
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:
> 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  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
> >>
> >
> > ___
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-- 
Petr Baudis
If you have good ideas, good data and fast computers,
you can do almost anything. -- Geoffrey Hinton
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Re: [Computer-go] CPU vs GPU

2016-03-02 Thread Chaz G. 
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  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
>>
>
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[Computer-go] CPU vs GPU

2016-03-01 Thread Rémi Coulom 

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
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