I was reading a linux mag article [1] saying that the latest nvidia GPUs
[2] solve many of the problems of using them for supercomputing problems.
There was a thread [3] here in September about running go playouts on
GPUs, where the people who had tried it seemed generally pessimistic. I
just
Darren,
these articles are still somewhat short on detail, so it's hard to tell.
A lot of the new features listed there won't have any impact on the
suitability of the GPU for Go, because they do not change the method of
computation (e.g. doubling floating point precision is irrelevant).
these articles are still somewhat short on detail, so it's hard to tell.
Yes the linux mag article was a bit empty wasn't it, but did you take a
look at the 20-page whitepaper:
http://www.nvidia.com/content/PDF/fermi_white_papers/NVIDIA_Fermi_Compute_Architecture_Whitepaper.pdf
Having said
I just wondered if this new Fermi GPU solves the issues for go
playouts, or don't really make any difference?
My first impression of Fermi is very positive. Fermi contains a lot of
features that make general purpose computing on a GPU much easier and better
performing.
However, it remains the
In my own gpu experiment (light playouts), registers/memory were the
bounding factors on simulation speed. I expected branching to affect it more
but as long as you have null branches (instead of branches that do
something) then the total execution only takes as long as the longest
branch, which
In my own gpu experiment (light playouts), registers/memory were the
bounding factors on simulation speed.
I respect your experimental finding, but I note that you have carefully
specified light playouts, probably because you suspect that there may be a
significant difference if playouts are
On Fri, Oct 23, 2009 at 01:34:29PM -0600, Brian Sheppard wrote:
I have not done any GPU experiments, so readers should take my guesswork
FWIW. I think the code that is light is the only piece that parallelizes
efficiently. Heavy playouts look for rare but important situations and
handle them
BTW, it occurs to me that we can approximate the efficiency of
parallelization by taking execution counts from a profiler and
post-processing them. I should do that before buying a new GPU. :-)
I wonder what you mean by that.
If you run your program on a sequential machine and count
