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===== Lammps GPU Testing (EC) =====
* 32 cores E2660
* 4 K20 GPU
* workstation
* MPICH2 flavor
Same tests (12 cpu cores) using lj/cut, eam, lj/expand, and morse: **AU.reduced**
CPU only 6 mins 1 secs
1 GPU 1 mins 1 secs (a 5-6 times speed up)
2 GPUs 1 mins 0 secs (never saw 2nd GPU used, problem set too small?)
Same tests (12 cpu cores) using a restart file and using gayberne: **GB**
CPU only 1 hour 5 mins
1 GPU 5 mins and 15 secs (a 18-19 times peed up)
2 GPUs 2 mins
Above results seems overall a bit slower that at other vendor, but same pattern.
Francis's Melt problem set
^3d Lennard-Jones melt: for 10,000 steps with 32,000 atoms^^^^^^
|CPU only| -np 1 | -np 6 | -np 12 | -np 24 | -np 36 |
|loop times| 329s | 63s | 39s | 29s | 45s |
|GPU only| 1xK20 | 2xK20 | 3xK20 | 4xK20 | (-np 1-4) |
|loop times| 28s | 16s | 11s | 10s | |
^3d Lennard-Jones melt: for 100,000 steps with 32,000 atoms^^^^^^
|GPU only| 1xK20 | 2xK20 | 3xK20 | 4xK20 | (-np 1-4) |
|loop times| 274s | 162s | 120s | 98s | |
* Serial's time of 329s is reduced to 29s for MPI, an 11x speed up
* GPU's serial time matches MPI -np 24 and can be further reduced to 10s, a 3x speed up
==== Redoing Above ====
**10/16/2013**
Redoing the melt problem now on our own K20 hardware I get the following (observing with gpu-info that utilization runs about 20-25% on the GPU allocated)
Loop time of 345.936 on 1 procs for 100000 steps with 32000 atoms
#!/bin/bash
# submit via 'bsub < run.gpu'
rm -f log.lammps melt.log
#BSUB -e err
#BSUB -o out
#BSUB -q mwgpu
#BSUB -J test
## 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 6144 MB (5 GB + 20%) memory per GPU
## run all processes (1<=n<=4)) on same node (hosts=1).
#BSUB -n 1
#BSUB -R "rusage[gpu=1:mem=6144],span[hosts=1]"
# from greentail we need to recreate module env
export PATH=/home/apps/bin:/cm/local/apps/cuda50/libs/304.54/bin:\
/cm/shared/apps/cuda50/sdk/5.0.35/bin/linux/release:/cm/shared/apps/lammps/cuda/2013-01-27/:\
/cm/shared/apps/amber/amber12/bin:/cm/shared/apps/namd/ibverbs-smp-cuda/2013-06-02/:\
/usr/lib64/qt-3.3/bin:/usr/local/bin:/bin:/usr/bin:/usr/local/sbin:/usr/sbin:/sbin:/sbin:\
/usr/sbin:/cm/shared/apps/cuda50/toolkit/5.0.35/bin:\
/cm/shared/apps/cuda50/sdk/5.0.35/bin/linux/release:/cm/shared/apps/cuda50/libs/current/bin:\
/cm/shared/apps/cuda50/toolkit/5.0.35/open64/bin:/cm/shared/apps/mvapich2/gcc/64/1.6/bin:\
/cm/shared/apps/mvapich2/gcc/64/1.6/sbin
export PATH=/share/apps/bin:$PATH
export LD_LIBRARY_PATH=/cm/local/apps/cuda50/libs/304.54/lib64:\
/cm/shared/apps/cuda50/toolkit/5.0.35/lib64:/cm/shared/apps/amber/amber12/lib:\
/cm/shared/apps/amber/amber12/lib64:/cm/shared/apps/namd/ibverbs-smp-cuda/2013-06-02/:\
/cm/shared/apps/cuda50/toolkit/5.0.35/lib64:/cm/shared/apps/cuda50/libs/current/lib64:\
/cm/shared/apps/cuda50/toolkit/5.0.35/open64/lib:\
/cm/shared/apps/cuda50/toolkit/5.0.35/extras/CUPTI/lib:\
/cm/shared/apps/mvapich2/gcc/64/1.6/lib
# unique job scratch dirs
MYSANSCRATCH=/sanscratch/$LSB_JOBID
MYLOCALSCRATCH=/localscratch/$LSB_JOBID
export MYSANSCRATCH MYLOCALSCRATCH
cd $MYSANSCRATCH
# LAMMPS
# GPUIDX=1 use allocated GPU(s), GPUIDX=0 cpu run only (view header au.inp)
export GPUIDX=1
# stage the data
cp ~/gpu_testing/fstarr/lj/* .
# feed the wrapper
lava.mvapich2.wrapper lmp_nVidia \
-c off -var GPUIDX $GPUIDX -in in.melt
# save results
cp log.lammps melt.log ~/gpu_testing/fstarr/lj/
===== Lammps GPU Testing (MW) =====
Vendor: "There are currently two systems available, each with two 8-core Xeon E5-2670 processors, 32GB memory, 120GB SSD and two Tesla K20 GPUs. The hostnames are master and node2.
You will see that a GPU-accelerated version of LAMMPS with MPI support is installed in /usr/local/LAMMPS."
Actually, turns out there are 32 cores on node so I suspect four CPUs.
First, we expose the GPUs to Lammps (so running with a value of -1 ignores the GPUs) in our input file.
# Enable GPU's if variable is set.
if "(${GPUIDX} >= 0)" then &
"suffix gpu" &
"newton off" &
"package gpu force 0 ${GPUIDX} 1.0"
Then we invoke the Lammps executable with MPI.
NODES=1 # number of nodes [=>1]
GPUIDX=0 # GPU indices range from [0,1], this is the upper bound.
# set GPUIDX=0 for 1 GPU/node or GPUIDX=1 for 2 GPU/node
CORES=12 # Cores per node. (i.e. 2 CPUs with 6 cores ea =12 cores per node)
which mpirun
echo "*** GPU run with one MPI process per core ***"
date
mpirun -np $((NODES*CORES)) -bycore ./lmp_ex1 -c off -var GPUIDX $GPUIDX \
-in film.inp -l film_1_gpu_1_node.log
date
Some tests using **lj/cut**, **eam**, **lj/expand**, and **morse**:
* CPU only 4 mins 30 secs
* 1 GPU 0 mins 47 secs (a 5-6 times speed up)
* 2 GPUs 0 mins 46 secs (never saw 2nd GPU used, problem set too small?)
Some tests using a restart file and using **gayberne**,
* CPU only 1 hour 5 mins
* 1 GPU 3 mins and 33 secs (a 18-19 times peed up)
* 2 GPUs 2 mins (see below)
node2$ gpu-info
====================================================
Device Model Temperature Utilization
====================================================
0 Tesla K20m 36 C 96 %
1 Tesla K20m 34 C 92 %
====================================================
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