Hi Filip,
> The only think that is a bit confusing for me is the scale. According
> to the benchmark the scale between one and 2xM2090 is just 19%, but
> between 2xM2090 and 4xM2090 is 50%, what could be the reason? Actually
I need to look more closely into these benchmarks to be sure. It is possible
the 2xM2090 is artificially slow for some reason. I'll have more confirmed
benchmarks shortly once the grant writing season is out of the way.
> from these numbers the scale for all systems is a bit worse compared to
> the previous benchmark results. Is that due to the new hardware (the
> new M2090 cards) or due to the new code changes?
It is unfortunately the laws of physics. Making the code run 2x faster on a
single GPU is ALWAYS going to come at the cost of scalability unless one
also doubles the interconnect speed (and reduces the latency) at the same
time which of course does not happen. Also the faster a single card gets
(such as the M2090 vs C2070) for a given interconnect speed (PCI-E x16 and
QDR IB) so the ratio of compute performance to interconnect performance gets
more unbalanced and scaling falls.
We are hoping to take advantage of new features for communicating between
GPUs in future releases of the NVIDIA toolkit that will help improve
scalability further. My ultimate goal right now is something approach half a
microsecond a day for the JAC benchmark. Whether this ultimately happens or
not and when will very much depend on whether NSF chooses to keep funding
this effort.
All the best
Ross
/\
\/
|\oss Walker
---------------------------------------------------------
| Assistant Research Professor |
| San Diego Supercomputer Center |
| Adjunct Assistant Professor |
| Dept. of Chemistry and Biochemistry |
| University of California San Diego |
| NVIDIA Fellow |
|
http://www.rosswalker.co.uk |
http://www.wmd-lab.org/ |
| Tel: +1 858 822 0854 | EMail:- ross.rosswalker.co.uk |
---------------------------------------------------------
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Received on Thu May 19 2011 - 14:30:02 PDT
