Hi Szilard,
Thanks for your suggestions. I am indeed aware of this page. In a 8-core
AMD with 1GPU, I am very happy about its performance. See below. My
intention is to obtain a even better one because we have multiple nodes.
### 8 core AMD with 1 GPU,
Force evaluation time GPU/CPU: 4.006 ms/2.578 ms = 1.554
For optimal performance this ratio should be close to 1!
NOTE: The GPU has >20% more load than the CPU. This imbalance causes
performance loss, consider using a shorter cut-off and a finer PME
grid.
Core t (s) Wall t (s) (%)
Time: 216205.510 27036.812 799.7
7h30:36
(ns/day) (hour/ns)
Performance: 31.956 0.751
### 8 core AMD with 2 GPUs
Core t (s) Wall t (s) (%)
Time: 178961.450 22398.880 799.0
6h13:18
(ns/day) (hour/ns)
Performance: 38.573 0.622
Finished mdrun on node 0 Sat Jul 13 09:24:39 2013
>However, in your case I suspect that the
>bottleneck is multi-threaded scaling on the AMD CPUs and you should
>probably decrease the number of threads per MPI rank and share GPUs
>between 2-4 ranks.
OK but can you give a example of mdrun command ? given a 8 core AMD with 2
GPUs.
I will try to run it again.
>Regarding scaling across nodes, you can't expect much from gigabit
>ethernet - especially not from the cheaper cards/switches, in my
>experience even reaction field runs don't scale across nodes with 10G
>ethernet if you have more than 4-6 ranks per node trying to
>communicate (let alone with PME). However, on infiniband clusters we
>have seen scaling to 100 atoms/core (at peak).
>From your comments, it sounds like a cluster of AMD cpus is difficult to
scale across nodes in our current setup.
Let's assume we install Infiniband (20 or 40GB/s) in the same system of 16
nodes of 8 core AMD with 1 GPU only. Considering the same AMD system, what
is a good way to obtain better performance when we run a task across nodes
? in other words, what dose mudrun_mpi look like ?
Thanks,
Dwey
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