[Users] Einstein Toolkit and modern AMD supercomputer
bozzola.gabriele at gmail.com
Fri Aug 27 11:44:51 CDT 2021
Last week I opened a PR to add the configuration files
for Expanse to simfactory. Expanse is an example of
the new generation of AMD supercomputers. Others are
Anvil, one of the other new XSEDE machines, or Puma,
the newest cluster at The University of Arizona.
I have some experience with Puma and Expanse and
I would like to share some thoughts, some of which come
from interacting with the admins of Expanse. The problem
is that I am finding terrible multi-node performance on both
these machines, and I don't know if this will be a common
thread among new AMD clusters.
These supercomputers have similar characteristics.
First, they have very high cores/node count (typically
128/node) but low memory per core (typically 2 GB / core).
In these conditions, it is very easy to have a job killed by
the OOM daemon. My suspicion is that it is rank 0 that
goes out of memory, and the entire run is aborted.
Second, depending on the MPI implementation, MPI collective
operations can be extremely expensive. I was told that
the best implementation is mvapich 2.3.6 (at the moment).
This seems to be due to the high core count.
I found that the code does not scale well. This is possibly
related to the previous point. If your job can fit on a single node,
it will run wonderfully. However, when you perform the same
simulation on two nodes, the code will actually be slower.
This indicates that there's no strong scaling at all from
1 node to 2 (128 to 256 cores, or 32 to 64 MPI ranks).
Using mvapich 2.3.6 improves the situation, but it is still
faster to use fewer nodes.
(My benchmark is a par file I've tested extensively on Frontera)
I am working with Expanse's support staff to see what we can
do, but I wonder if anyone has had a positive experience with
this architecture and has some tips to share.
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