Re: [agi] NVIDIA GPU's

2007-06-22 Thread J Storrs Hall, PhD 
BTW, the CUDA toolkit for programming the GPU's is developing rapidly (and is 
still in beta). here are memory bandwidths actually measured on my machine:

CUDA version 0.8:

Host to Device Bandwidth for Pinned memory
Transfer Size (Bytes)   Bandwidth(MB/s)
 33554432   1647.6

Device to Host Bandwidth for Pinned memory
Transfer Size (Bytes)   Bandwidth(MB/s)
 33554432   1654.7

Device to Device Bandwidth
Transfer Size (Bytes)   Bandwidth(MB/s)
 33554432   3332.1

CUDA version 0.9:

Host to Device Bandwidth for Pinned memory
Transfer Size (Bytes)   Bandwidth(MB/s)
 33554432   2700.0

Device to Host Bandwidth for Pinned memory
Transfer Size (Bytes)   Bandwidth(MB/s)
 33554432   2693.3

Device to Device Bandwidth
Transfer Size (Bytes)   Bandwidth(MB/s)
 33554432   53768.0

In other words, once uploaded to the GPU, you can afford to reorder your data 
any way you want as often as you need to to take advantage of parallel ops.

Or to put it another way, the GPU has about the same bandwidth to its memory 
as your brain does to its on a per-byte basis, in my ballpark estimates. 
Somewhere on the order of 100 GPUs is a brain-equivalent. 

We're getting damn close...

Josh

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Re: [agi] NVIDIA GPU's

2007-06-22 Thread Bo Morgan 

That's 53.8 GB/s for a load of 33.6 MB?  Is there a burst cache effect 
going on here or do you think that's sustainable for multiple seconds?

Bo

On Fri, 22 Jun 2007, J Storrs Hall, PhD wrote:

) BTW, the CUDA toolkit for programming the GPU's is developing rapidly (and is 
) still in beta). here are memory bandwidths actually measured on my machine:
) 
) CUDA version 0.8:
) 
) Host to Device Bandwidth for Pinned memory
) Transfer Size (Bytes)   Bandwidth(MB/s)
)  33554432   1647.6
) 
) Device to Host Bandwidth for Pinned memory
) Transfer Size (Bytes)   Bandwidth(MB/s)
)  33554432   1654.7
) 
) Device to Device Bandwidth
) Transfer Size (Bytes)   Bandwidth(MB/s)
)  33554432   3332.1
) 
) CUDA version 0.9:
) 
) Host to Device Bandwidth for Pinned memory
) Transfer Size (Bytes)   Bandwidth(MB/s)
)  33554432   2700.0
) 
) Device to Host Bandwidth for Pinned memory
) Transfer Size (Bytes)   Bandwidth(MB/s)
)  33554432   2693.3
) 
) Device to Device Bandwidth
) Transfer Size (Bytes)   Bandwidth(MB/s)
)  33554432   53768.0
) 
) In other words, once uploaded to the GPU, you can afford to reorder your data 
) any way you want as often as you need to to take advantage of parallel ops.
) 
) Or to put it another way, the GPU has about the same bandwidth to its memory 
) as your brain does to its on a per-byte basis, in my ballpark estimates. 
) Somewhere on the order of 100 GPUs is a brain-equivalent. 
) 
) We're getting damn close...
) 
) Josh
) 
) -
) This list is sponsored by AGIRI: http://www.agiri.org/email
) To unsubscribe or change your options, please go to:
) http://v2.listbox.com/member/?&;
) 

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Re: [agi] NVIDIA GPU's

2007-06-22 Thread J Storrs Hall, PhD 
On Friday 22 June 2007 11:22:19 am Bo Morgan wrote:
> 
> That's 53.8 GB/s for a load of 33.6 MB?  Is there a burst cache effect 
> going on here or do you think that's sustainable for multiple seconds?
> 
> Bo

./bandwidthTest --mode=range --dtod --start=1000 --end=2 
--increment=1000
Range Mode
Device to Device Bandwidth

Transfer Size (Bytes)   Bandwidth(MB/s)
 1000   47935.4
 2000   47845.2
 3000   48923.1
 4000   49836.0
 5000   52602.0
 6000   53740.7
 7000   54015.1
 8000   53912.3
 9000   53952.7
1   54050.9
11000   54001.9
12000   53965.0
13000   54010.2
14000   54089.5
15000   54026.4
16000   54019.1
17000   54067.7
18000   54110.9
19000   54004.4
2   54075.4

If I understand the specs right, there's a 320 bit wide data bus to on-card 
memory. The major hangup is that there just isn't that much (640MB on my GTS 
cards, 1.5GB on the Tesla) memory to start with.

Josh

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[agi] AGI introduction

2007-06-22 Thread Pei Wang 

Hi,

I put a brief introduction to AGI at
http://nars.wang.googlepages.com/AGI-Intro.htm ,  including an "AGI
Overview" followed by "Representative AGI Projects".

It is basically a bunch of links and quotations organized according to
my opinion. Hopefully it can help some newcomers to get a big picture
of the idea and the field.

Pei

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Re: [agi] AGI introduction

2007-06-22 Thread Lukasz Stafiniak 

On 6/22/07, Pei Wang <[EMAIL PROTECTED]> wrote:

Hi,

I put a brief introduction to AGI at
http://nars.wang.googlepages.com/AGI-Intro.htm ,  including an "AGI
Overview" followed by "Representative AGI Projects".


Thanks! As a first note, SAIL seems to me a better replacement for
Cog, because SAIL has much generality and some theoretical
accomplishment where Cog is (AFAIK) hand-crafted engineering.

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Re: [agi] AGI introduction

2007-06-22 Thread Mike Tintner 


Pei:> I put a brief introduction to AGI at

http://nars.wang.googlepages.com/AGI-Intro.htm ,  including an "AGI
Overview" followed by "Representative AGI Projects".


Very helpful. Thankyou.

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[agi] Association for Uncertainty in Artificial Intelligence

2007-06-22 Thread Lukasz Stafiniak 

Looking through Wikipedia articles I stumbled upon a probably very
interesting place:
http://www.auai.org/
Association for Uncertainty in Artificial Intelligence

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[agi] "Reinforcement Learning: An Introduction" Richard S. Sutton and Andrew G. Barto

2007-06-22 Thread Lukasz Stafiniak 

Obligatory reading:
http://www.cs.ualberta.ca/~sutton/book/ebook/the-book.html

Cheers.

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Re: [agi] AGI introduction

2007-06-22 Thread Pei Wang 

On 6/22/07, Lukasz Stafiniak <[EMAIL PROTECTED]> wrote:


As a first note, SAIL seems to me a better replacement for
Cog, because SAIL has much generality and some theoretical
accomplishment where Cog is (AFAIK) hand-crafted engineering.


In many aspects, I agree that SAIL is more interesting than Cog.

I include Cog in the list, because it is explicitly based on a theory
about intelligence as a whole (see
http://groups.csail.mit.edu/lbr/hrg/1998/group-AAAI-98.pdf), while in
SAIL such a theory is not very clear. Of course, this boundary is
fuzzy, so I may include SAIL in a future version of the list,
depending on the development of the project.

Pei

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Re: [agi] "Reinforcement Learning: An Introduction" Richard S. Sutton and Andrew G. Barto

2007-06-22 Thread Bo Morgan 

You make AGI sound like a "members only" club by this "obligatory" 
comment. ;)

Reinforcement learning is a simple theory that only solves problems for 
which we can design value functions.

We need some good readings about how to organize better programs.  Books 
on how to program large complicated systems with many interconnected 
parts.

Bo

On Fri, 22 Jun 2007, Lukasz Stafiniak wrote:

) Obligatory reading:
) http://www.cs.ualberta.ca/~sutton/book/ebook/the-book.html
) 
) Cheers.
) 
) -
) This list is sponsored by AGIRI: http://www.agiri.org/email
) To unsubscribe or change your options, please go to:
) http://v2.listbox.com/member/?&;
) 

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Re: [agi] "Reinforcement Learning: An Introduction" Richard S. Sutton and Andrew G. Barto

2007-06-22 Thread Lukasz Stafiniak 

On 6/23/07, Bo Morgan <[EMAIL PROTECTED]> wrote:


Reinforcement learning is a simple theory that only solves problems for
which we can design value functions.


But it is good for AGI newbies like me to start with :-)

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