You can look at the benchmarks on the amber page, though you have to go far
back to get k40 numbers. See here and compare to your results
https://ambermd.org/gpus16/benchmarks.htm
On Wed, Feb 19, 2020, 7:21 AM Aravind R <aravindspg27.gmail.com> wrote:
> Dear AmberUsers,
> I am running my REMD simulations on an SGE cluster with 62 nodes with each
> node having 2 Tesla K40m.
> I use the following input script to run the REMD simulation in
> pmem.cuda.MPI and follow the same procedure as in "*Folding Simulations for
> Proteins with Diverse Topologies Are Accessible in Days with a
> Physics-Based Force Field and Implicit Solvent*" :
>
> #!/bin/bash
> #$ -N D1_REMD
> #$ -S /bin/bash
> #$ -cwd
> #$ -V
> #$ -q all.q
> #$ -pe mpirun 12
> export AMBERHOME=/home/rana/aravindr/amber18
> source $AMBERHOME/amber.sh
> export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_HOME/lib:$CUDA_HOME/lib64
> export
>
> LD_LIBRARY_PATH=/softwares/cuda-8.0/lib:/softwares/cuda-8.0/lib64:$LD_LIBRARY_PATH
> export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/openmpi-1.8.2/bin/mpirun
> cd $SGE_O_WORKDIR
> export CUDA_VISIBLE_DEVICES="0,1"
>
> mpirun=~/openmpi-1.8.2/bin/mpirun
> sander_mpi=/home/rana/aravindr/amber18/bin/sander.MPI
> pmemd_cuda=/home/rana/aravindr/amber18/bin/pmemd.cuda
> pmemd_mpi=/home/rana/aravindr/amber18/bin/pmemd.MPI
> cpptraj_mpi=/home/rana/aravindr/amber18/bin/cpptraj.MPI
> pmemd_cuda_mpi=/home/rana/aravindr/amber18/bin/pmemd.cuda.MPI
>
> echo "Current working directory: `pwd`"
> $mpirun -np 12 $pmemd_cuda_mpi -ng 12 -groupfile remd.groupfile
>
> I see that in the paper they are able to achieve 800ns/day(system with
> 73AA) for a protein of similar size, whereas I am able to get only
> 77ns/day(system with 78AA) even after using HMR with the same number of
> replicas. Is there something wrong with my pmemd.cuda.MPI implementation or
> should I expect only this much performance from Tesla K40 cards?
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>
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Received on Wed Feb 19 2020 - 04:30:02 PST
