LAMMPS benchmark problems

This directory contains 5 benchmark problems which are discussed in
the Benchmark section of the LAMMPS documentation, and on the
Benchmark page of the LAMMPS WWW site (lammps.sandia.gov/bench).

This directory also has several sub-directories:

FERMI           benchmark scripts for desktop machine with Fermi GPUs (Tesla)
KEPLER          benchmark scripts for GPU cluster with Kepler GPUs
POTENTIALS      benchmarks scripts for various potentials in LAMMPS

The results for all of these benchmarks are displayed and discussed on
the Benchmark page of the LAMMPS WWW site: lammps.sandia.gov/bench.

The remainder of this file refers to the 5 problems in the top-level
of this directory and how to run them on CPUs, either in serial or
parallel.  The sub-directories have their own README files which you
should refer to before running those scripts.

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Each of the 5 problems has 32,000 atoms and runs for 100 timesteps.
Each can be run as a serial benchmark (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.

A few sample log file outputs on different machines and different
numbers of processors are included in this directory to compare your
answers to.  E.g. a log file like log.date.chain.lmp.scaled.foo.P is
for a scaled-size version of the Chain benchmark, run on P processors
of machine "foo" with the dated version of LAMMPS.  Note that the Eam
and Lj benchmarks may not give identical answers on different machines
because of the "velocity loop geom" option that assigns velocities
based on atom coordinates - see the discussion in the documentation
for the velocity command for details.

The CPU time (in seconds) for the run is in the "Loop time" line
of the log files, e.g.

Loop time of 3.89418 on 8 procs for 100 steps with 32000 atoms

Timing results for these problems run on various machines are listed
on the Benchmarks page of the LAMMPS WWW Site.

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These are the 5 benchmark problems:

LJ = atomic fluid, Lennard-Jones potential with 2.5 sigma cutoff (55
neighbors per atom), NVE integration

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

EAM = metallic solid, Cu EAM potential with 4.95 Angstrom cutoff (45
neighbors per atom), NVE integration

Chute = granular chute flow, frictional history potential with 1.1
sigma cutoff (7 neighbors per atom), NVE integration

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

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Here is a src/Make.py command which will perform a parallel build of a
LAMMPS executable "lmp_mpi" with all the packages needed by all the
examples.  This assumes you have an MPI installed on your machine so
that "mpicxx" can be used as the wrapper compiler.  It also assumes
you have an Intel compiler to use as the base compiler.  You can leave
off the "-cc mpi wrap=icc" switch if that is not the case.  You can
also leave off the "-fft fftw3" switch if you do not have the FFTW
(v3) installed as an FFT package, in which case the default KISS FFT
library will be used.

cd src
Make.py -j 16 -p none molecule manybody kspace granular rigid orig \
  -cc mpi wrap=icc -fft fftw3 -a file mpi

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Here is how to run each problem, assuming the LAMMPS executable is
named lmp_mpi, and you are using the mpirun command to launch parallel
runs:

Serial (one processor runs):

lmp_mpi < in.lj
lmp_mpi < in.chain
lmp_mpi < in.eam
lmp_mpi < in.chute
lmp_mpi < in.rhodo

Parallel fixed-size runs (on 8 procs in this case):

mpirun -np 8 lmp_mpi < in.lj
mpirun -np 8 lmp_mpi < in.chain
mpirun -np 8 lmp_mpi < in.eam
mpirun -np 8 lmp_mpi < in.chute
mpirun -np 8 lmp_mpi < in.rhodo

Parallel scaled-size runs (on 16 procs in this case):

mpirun -np 16 lmp_mpi -var x 2 -var y 2 -var z 4 < in.lj
mpirun -np 16 lmp_mpi -var x 2 -var y 2 -var z 4 < in.chain.scaled
mpirun -np 16 lmp_mpi -var x 2 -var y 2 -var z 4 < in.eam
mpirun -np 16 lmp_mpi -var x 4 -var y 4 < in.chute.scaled
mpirun -np 16 lmp_mpi -var x 2 -var y 2 -var z 4 < in.rhodo.scaled

For each of the scaled-size runs you must set 3 variables as -var
command line switches.  The variables x,y,z are used in the input
scripts to scale up the problem size in each dimension.  Imagine the P
processors arrayed as a 3d grid, so that P = Px * Py * Pz.  For P =
16, you might use Px = 2, Py = 2, Pz = 4.  To scale up equally in all
dimensions you roughly want Px = Py = Pz.  Using the var switches, set
x = Px, y = Py, and z = Pz.

For Chute runs, you must have Pz = 1.  Therefore P = Px * Py and you
only need to set variables x and y.