# Benchmarks

Current LAMMPS performance is discussed on the [Benchmarks
page](https://www.lammps.org/bench.html)\_ of the [LAMMPS
website](https://www.lammps.org)\_ where timings and parallel efficiency
are listed. The page has several sections, which are briefly described
below:

-   CPU performance on 5 standard problems, strong and weak scaling
-   GPU and Xeon Phi performance on same and related problems
-   Comparison of cost of interatomic potentials
-   Performance of huge, billion-atom problems

The 5 standard problems are as follow:

1.  LJ = atomic fluid, Lennard-Jones potential with 2.5 sigma cutoff (55
    neighbors per atom), NVE integration
2.  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
3.  EAM = metallic solid, Cu EAM potential with 4.95 Angstrom cutoff (45
    neighbors per atom), NVE integration
4.  Chute = granular chute flow, frictional history potential with 1.1
    sigma cutoff (7 neighbors per atom), NVE integration
5.  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

Input files for these 5 problems are provided in the bench directory of
the LAMMPS distribution. Each has 32,000 atoms and runs for 100
timesteps. The size of the problem (number of atoms) can be varied using
command-line switches as described in the bench/README file. This is an
easy way to test performance and either strong or weak scalability on
your machine.

The bench directory includes a few log.\* files that show performance of
these 5 problems on 1 or 4 cores of Linux desktop. The bench/FERMI and
bench/KEPLER directories have input files and scripts and instructions
for running the same (or similar) problems using OpenMP or GPU or Xeon
Phi acceleration options. See the README files in those directories and
the [Accelerator packages](Speed_packages) pages for instructions on how
to build LAMMPS and run on that kind of hardware.

The bench/POTENTIALS directory has input files which correspond to the
table of results on the
[Potentials](https://www.lammps.org/bench.html#potentials)\_ section of
the Benchmarks web page. So you can also run those test problems on your
machine.

The [billion-atom](https://www.lammps.org/bench.html#billion)\_ section
of the Benchmarks web page has performance data for very large benchmark
runs of simple Lennard-Jones (LJ) models, which use the bench/in.lj
input script.

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For all the benchmarks, a useful metric is the CPU cost per atom per
timestep. Since performance scales roughly linearly with problem size
and timesteps for all LAMMPS models (i.e. interatomic or coarse-grained
potentials), the run time of any problem using the same model (atom
style, force field, cutoff, etc) can then be estimated.

Performance on a parallel machine can also be predicted from one-core or
one-node 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 a parallel
efficiency on these benchmarks above 50% so long as the number of
atoms/core is a few 100 or greater, and closer to 100% for large numbers
of atoms/core. This is for all-MPI mode with one MPI task per core. For
nodes with accelerator options or hardware (OpenMP, GPU, Phi), you
should first measure single node performance. Then you can estimate
parallel performance for multi-node runs using the same logic as for
all-MPI mode, except that now you will typically need many more
atoms/node to achieve good scalability.