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K20 Redo Usage

One node n37 has been redone with latest Nvidia CUDA drives during summer 2018. Please test it out before we decide to redo all of them. It is running CentOS 7.5 and I'm interested to see if programs compiled under 6.x or 5.x break.

The node is in “inactive” state so your job will go pending. I will force them through.

Usage is about the same as jobs going to the amber128 queue with two minor changes:

  • openmpi instead mpich
  • different cuda architecture than before –arch=sm_35 (vs sm_21)

I also bypassed the Amber nvidia version check so we're running in unsupported mode.

Please check your new results against previous output .

Details on how the environment was setup

Here is a submit script for recompiled local versions of Amber, Gromacs and Lammps using a custom wrapper.

/home/hmeij/k20redo/run.sh

#!/bin/bash
# submit via 'bsub < run.sh'
rm -f out err 
#BSUB -e err
#BSUB -o out
#BSUB -q mwgpu
#BSUB -J "K20 test"
#BSUB -m n37

# cuda 9 & openmpi
export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export PATH=/share/apps/CENTOS6/openmpi/1.8.4/bin:$PATH
export LD_LIBRARY_PATH=/share/apps/CENTOS6/openmpi/1.8.4/lib:$LD_LIBRARY_PATH


## leave sufficient time between job submissions (30-60 secs)
## the number of GPUs allocated matches -n value automatically
## always reserve GPU (gpu=1), setting this to 0 is a cpu job only
## reserve 12288 MB (11 GB + 1 GB overhead) memory per GPU
## run all processes (1<=n<=4)) on same node (hosts=1).


# unique job scratch dirs
MYSANSCRATCH=/sanscratch/$LSB_JOBID
MYLOCALSCRATCH=/localscratch/$LSB_JOBID
export MYSANSCRATCH MYLOCALSCRATCH
cd $MYLOCALSCRATCH


# uncomment one software block by removing ONLY one # on each line


## AMBER we need to recreate env, $AMBERHOME is already set
##BSUB -n 1
##BSUB -R "rusage[gpu=1:mem=12288],span[hosts=1]"
#export PATH=/share/apps/CENTOS6/python/2.7.9/bin:$PATH
#export LD_LIBRARY_PATH=/share/apps/CENTOS6/python/2.7.9/lib:$LD_LIBRARY_PATH
#source /usr/local/amber16/amber.sh
## stage the data
#cp -r ~/sharptail/* .
## feed the wrapper
#n37.openmpi.wrapper pmemd.cuda.MPI \
#-O -o mdout.$LSB_JOBID -inf mdinfo.1K10 -x mdcrd.1K10 -r restrt.1K10 -ref inpcrd
## save results
#scp mdout.$LSB_JOBID ~/k20redo/


## GROMACS (using all GPUs example)
##BSUB -n 4
##BSUB -R "rusage[gpu=4:mem=49152],span[hosts=1]"
#export CPU_GPU_REQUEST=4:4
## signal GMXRC is a gpu run with: 1=thread_mpi 2=openmpi
#export GMXRC=2
#export PATH=/usr/local/gromacs-2018/bin:$PATH
#export LD_LIBRARY_PATH=/usr/local/gromacs-2018/lib64:$LD_LIBRARY_PATH
#. /usr/local/gromacs-2018/bin/GMXRC.bash
#cd /home/hmeij/gromacs_bench/gpu/
#n37.openmpi.wrapper gmx_mpi mdrun \
#  -maxh 0.5 -nsteps 600000 -multidir 01 02 03 04 -gpu_id 0123 \
#  -ntmpi 0 -npme 0 -s topol.tpr -ntomp 0 -pin on -nb gpu



## LAMMPS
##BSUB -n 1
##BSUB -R "rusage[gpu=1:mem=12288],span[hosts=1]"
## GPUIDX=1 use allocated GPU(s), GPUIDX=0 cpu run only (view header input file)
#export GPUIDX=1 # use with -var $GPUIDX in inout file, view au.in, or use -suffix 
#export PATH=/usr/local/lammps-22Aug18:$PATH
## stage the data
#cp -r ~/sharptail/* .
## feed the wrapper
#n37.openmpi.wrapper lmp_mpi-double-double-with-cuda \
#-suffix gpu -var GPUIDX $GPUIDX -in in.colloid -l out.colloid.$LSB_JOBID
## save results
#scp out.colloid.$LSB_JOBID ~/k20redo/

cluster/173.1535135375.txt.gz · Last modified: 2018/08/24 14:29 by hmeij07