Difference between revisions of "Gromacs/Acceleration and parallelization"
From Computational Biophysics and Materials Science Group
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* N is the number of cores you wish to use in case a single machine is utilitzed | * N is the number of cores you wish to use in case a single machine is utilitzed | ||
* Dunno why M=32 can only utilize one single node, i.e. M=48 utilizes one and half node observed from Ganglia | * Dunno why M=32 can only utilize one single node, i.e. M=48 utilizes one and half node observed from Ganglia | ||
| − | * Therefore '''DO NOT''' specify total number of cores after mpirun. Specify using <nowiki>#</nowiki>PBS -l instead and let mpirun and mdrun decide by themselves | + | * Therefore '''DO NOT''' specify total number of cores after mpirun. Specify using <nowiki>#</nowiki>PBS -l instead and let mpirun and mdrun decide by themselves |
| + | * -pin on is used when N<16 | ||
==Gromacs 5.0.4 CUDA, single== | ==Gromacs 5.0.4 CUDA, single== | ||
Revision as of 13:29, 16 January 2015
Read this first.
On Combo, we have compiled several builds of Gromacs. List refers to How to use Combo and compilation options refer to How to compile Gromacs.
We now provide mdrun options for each build:
Contents
[hide]Gromacs 5.0.4 MPI, single, GPU
UNDER TESTS
Gromacs 5.0.4 MPI, double
- #PBS -l nodes=1;ppn=12
mpirun mdrun - mdrun -ntomp N -pin -on
Points to note: * N is the number of cores you wish to use in case a single machine is utilitzed * Dunno why M=32 can only utilize one single node, i.e. M=48 utilizes one and half node observed from Ganglia * Therefore DO NOT specify total number of cores after mpirun. Specify using #PBS -l instead and let mpirun and mdrun decide by themselves * -pin on is used when N<16
Gromacs 5.0.4 CUDA, single
mdrun (-nt N) (-pin on)
Points to note: * For single machine only, i.e. N<=16
Gromacs 5.0.4 thread-MPI, double
mdrun (-nt N) (-pin on)
Points to note: * For single machine only, i.e. N<=16
Benchmark
We compared difference between 12 MPI processes with 1 OPENMP thread each and 1 MPI process with 12 OPENMP threads. Though same amount of time is used, different distribution of time could be found.
