----- Original Message -----
From: "Jeff Nowakowski" <[EMAIL PROTECTED]>
To: "computer-go" <computer-go@computer-go.org>
Sent: Thursday, January 11, 2007 3:37 PM
Subject: Re: [computer-go] Gnugo vs commercial programs
On Thu, 2007-01-11 at 07:40 -0500, Don Dailey wrote:
Of course there is some questions
about how long Moore's law will hold.
If you are referring to CPU speed doubling (as opposed to transistor
count), then that has been over for at least 5 years.
"The Free Lunch Is Over: A Fundamental Turn Toward Concurrency in
Software"
http://www.gotw.ca/publications/concurrency-ddj.htm
The problem is that concurrency doesn't scale well.
-Jeff
Yes, the INTEL engineers have long solved the problems of the programmers.
But now the programmers have to solve the problems of the engineers. They do
not know what to do with additional gates. The simplest way is to add
another core. And if you have still too much gates left, make a quad core.
The problem is that concurrency doesn't scale well.
I think it depends on the application. In the simplest case, a server with
many processes, it scales well. There are other applications like graphics
were scaling is up to a certain limit relative straightforward. And there
are still other applications like Alpha-Beta search which scale badly.
Although up to 4 processors even Alpha-Beta scales well.
In the Hydra FPGA there are also enough gates for a 2nd chess-core. But
until now I have not succeeded to get a speedup with the second core. One
very nasty limiting factor is the slow PCI bus. The Software side can not
feed this cores fast enough (the CPU speed is sufficient, but bringing it
over the bus is the problem). Another problem is Alpha-Beta itself. The
FPGAs search with a fixed depth 4. If at Depth 4 nothing is to distribute,
because the first move creates already a cutoff, the second core sits idle.
The bus problem is a general one. E.g. modern graphic cards have a very
powerfull GPU. One could use this e.g. for the computation of neural
networks. The theoretic speedup is impressive, but the practical is low or
it even slows down things. The neural-network-computation must - in
comparision to the data - very large. Otherwise the transfer of data eats up
all the speedup.
Chrilly
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