The @home systems work great for big problems that do not have time
constraints. Game playing is interactive and people expect reasonably
quick replies. The problem with @home computational models is that you
never know when the user will want their machine back, so you have the
problem of deciding if a result is worth waiting for, or if you will
send similar requests to multiple machines just to try and be sure
that you get at least one reply.
I was contacted by someone in the Govt of Singapore about trying
exactly this ([EMAIL PROTECTED]) and while it is interesting, it is not nearly
as simple as SETI@ home, where independent problems are being solved
on different machines and you do not expect to get the answer back to
the primary server on any particular schedule. In Go the answers are
interdependent.
I suggested that it was too hard, or at best a research project not
nearly ready for prime-time.
Cheers,
David
On 2, Oct 2008, at 10:17 AM, Michael Markefka wrote:
So, when are we going to see distributed computing? [EMAIL PROTECTED],
[EMAIL PROTECTED], [EMAIL PROTECTED] With Go engines that scale well to increased
processing capacity, imagine facilitating a few thousand PCs to do
the computing. For good measure, [EMAIL PROTECTED] as about 800,000 nodes
online as of now.
What's the approximate increase in playing level per increase in
processing power? Any rough law for that?
Best regards,
Mike
Olivier Teytaud wrote:
Mogo was allowed to use 800 cores, not more, and only for games
against humans.
We have no acces to so many cores for computer-computer games (if
there were only three teams involved,
we could :-) ).
For some games Huygens was unaivalable at all, and mogo played with
much weaker hardware (some quad-cores,
however, it is not so bad :-) ).
Best regards,
Olivier
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