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<H3>6. Performance & scalability
</H3>
<P>LAMMPS performance on several prototypical benchmarks and machines is
discussed on the Benchmarks page of the <A HREF = "http://lammps.sandia.gov">LAMMPS WWW Site</A> where
CPU timings and parallel efficiencies are listed. Here, the
benchmarks are described briefly and some useful rules of thumb about
their performance are highlighted.
</P>
<P>These are the 5 benchmark problems:
</P>
<OL><LI>LJ = atomic fluid, Lennard-Jones potential with 2.5 sigma cutoff (55
neighbors per atom), NVE integration
<LI>Chain = bead-spring polymer melt of 100-mer chains, FENE bonds and LJ
pairwise interactions with a 2^(1/6) sigma cutoff (5 neighbors per
atom), NVE integration
<LI>EAM = metallic solid, Cu EAM potential with 4.95 Angstrom cutoff (45
neighbors per atom), NVE integration
<LI>Chute = granular chute flow, frictional history potential with 1.1
sigma cutoff (7 neighbors per atom), NVE integration
<LI>Rhodo = rhodopsin protein in solvated lipid bilayer, CHARMM force
field with a 10 Angstrom LJ cutoff (440 neighbors per atom),
particle-particle particle-mesh (PPPM) for long-range Coulombics, NPT
integration
</OL>
<P>The input files for running the benchmarks are included in the LAMMPS
distribution, as are sample output files. Each of the 5 problems has
32,000 atoms and runs for 100 timesteps. Each can be run as a serial
benchmarks (on one processor) or in parallel. In parallel, each
benchmark can be run as a fixed-size or scaled-size problem. For
fixed-size benchmarking, the same 32K atom problem is run on various
numbers of processors. For scaled-size benchmarking, the model size
is increased with the number of processors. E.g. on 8 processors, a
256K-atom problem is run; on 1024 processors, a 32-million atom
problem is run, etc.
</P>
<P>A useful metric from the benchmarks is the CPU cost per atom per
timestep. Since LAMMPS performance scales roughly linearly with
problem size and timesteps, the run time of any problem using the same
model (atom style, force field, cutoff, etc) can then be estimated.
For example, on a 1.7 GHz Pentium desktop machine (Intel icc compiler
under Red Hat Linux), the CPU run-time in seconds/atom/timestep for
the 5 problems is
</P>
<DIV ALIGN=center><TABLE BORDER=1 >
<TR ALIGN="center"><TD ALIGN ="right">Problem:</TD><TD > LJ</TD><TD > Chain</TD><TD > EAM</TD><TD > Chute</TD><TD > Rhodopsin</TD></TR>
<TR ALIGN="center"><TD ALIGN ="right">CPU/atom/step:</TD><TD > 4.55E-6</TD><TD > 2.18E-6</TD><TD > 9.38E-6</TD><TD > 2.18E-6</TD><TD > 1.11E-4</TD></TR>
<TR ALIGN="center"><TD ALIGN ="right">Ratio to LJ:</TD><TD > 1.0</TD><TD > 0.48</TD><TD > 2.06</TD><TD > 0.48</TD><TD > 24.5
</TD></TR></TABLE></DIV>
<P>The ratios mean that if the atomic LJ system has a normalized cost of
1.0, the bead-spring chains and granular systems run 2x faster, while
the EAM metal and solvated protein models run 2x and 25x slower
respectively. The bulk of these cost differences is due to the
expense of computing a particular pairwise force field for a given
number of neighbors per atom.
</P>
<P>Performance on a parallel machine can also be predicted from the
one-processor timings if the parallel efficiency can be estimated.
The communication bandwidth and latency of a particular parallel
machine affects the efficiency. On most machines LAMMPS will give
fixed-size parallel efficiencies on these benchmarks above 50% so long
as the atoms/processor count is a few 100 or greater - i.e. on 64 to
128 processors. Likewise, scaled-size parallel efficiencies will
typically be 80% or greater up to very large processor counts. The
benchmark data on the <A HREF = "http://lammps.sandia.gov">LAMMPS WWW Site</A> gives specific examples on
some different machines, including a run of 3/4 of a billion LJ atoms
on 1500 processors that ran at 85% parallel efficiency.
</P>
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