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<title>8. Performance & scalability — LAMMPS documentation</title>
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<a href="Manual.html" class="icon icon-home"> LAMMPS
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<li class="toctree-l1"><a class="reference internal" href="Section_intro.html">1. Introduction</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Section_start.html">2. Getting Started</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Section_commands.html">3. Commands</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Section_packages.html">4. Packages</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Section_accelerate.html">5. Accelerating LAMMPS performance</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Section_howto.html">6. How-to discussions</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Section_example.html">7. Example problems</a></li>
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<li class="toctree-l1 current"><a class="current reference internal" href="#">8. Performance & scalability</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Section_tools.html">9. Additional tools</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Section_modify.html">10. Modifying & extending LAMMPS</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Section_python.html">11. Python interface to LAMMPS</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Section_errors.html">12. Errors</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Section_history.html">13. Future and history</a></li>
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<div class="section" id="performance-scalability">
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<h1>8. Performance & scalability</h1>
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<p>Current LAMMPS performance is discussed on the Benchmarks page of the
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<a class="reference external" href="http://lammps.sandia.gov">LAMMPS WWW Site</a> where CPU timings and parallel efficiencies are
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listed. The page has several sections, which are briefly described
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below:</p>
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<ul class="simple">
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<li>CPU performance on 5 standard problems, strong and weak scaling</li>
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<li>GPU and Xeon Phi performance on same and related problems</li>
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<li>Comparison of cost of interatomic potentials</li>
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<li>Performance of huge, billion-atom problems</li>
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</ul>
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<p>The 5 standard problems are as follow:</p>
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<ol class="arabic simple">
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<li>LJ = atomic fluid, Lennard-Jones potential with 2.5 sigma cutoff (55
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neighbors per atom), NVE integration</li>
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<li>Chain = bead-spring polymer melt of 100-mer chains, FENE bonds and LJ
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pairwise interactions with a 2^(1/6) sigma cutoff (5 neighbors per
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atom), NVE integration</li>
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<li>EAM = metallic solid, Cu EAM potential with 4.95 Angstrom cutoff (45
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neighbors per atom), NVE integration</li>
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<li>Chute = granular chute flow, frictional history potential with 1.1
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sigma cutoff (7 neighbors per atom), NVE integration</li>
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<li>Rhodo = rhodopsin protein in solvated lipid bilayer, CHARMM force
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field with a 10 Angstrom LJ cutoff (440 neighbors per atom),
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particle-particle particle-mesh (PPPM) for long-range Coulombics, NPT
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integration</li>
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</ol>
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<p>Input files for these 5 problems are provided in the bench directory
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of the LAMMPS distribution. Each has 32,000 atoms and runs for 100
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timesteps. The size of the problem (number of atoms) can be varied
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using command-line switches as described in the bench/README file.
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This is an easy way to test performance and either strong or weak
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scalability on your machine.</p>
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<p>The bench directory includes a few log.* files that show performance
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of these 5 problems on 1 or 4 cores of Linux desktop. The bench/FERMI
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and bench/KEPLER dirs have input files and scripts and instructions
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for running the same (or similar) problems using OpenMP or GPU or Xeon
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Phi acceleration options. See the README files in those dirs and the
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<a class="reference internal" href="Section_accelerate.html"><span class="doc">Section accelerate</span></a> doc pages for
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instructions on how to build LAMMPS and run on that kind of hardware.</p>
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<p>The bench/POTENTIALS directory has input files which correspond to the
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table of results on the
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<span class="xref std std-ref">Potentials</span> section of
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the Benchmarks web page. So you can also run those test problems on
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your machine.</p>
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<p>The <span class="xref std std-ref">billion-atom</span> section
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of the Benchmarks web page has performance data for very large
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benchmark runs of simple Lennard-Jones (LJ) models, which use the
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bench/in.lj input script.</p>
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<hr class="docutils" />
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<p>For all the benchmarks, a useful metric is the CPU cost per atom per
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timestep. Since performance scales roughly linearly with problem size
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and timesteps for all LAMMPS models (i.e. inteatomic or coarse-grained
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potentials), the run time of any problem using the same model (atom
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style, force field, cutoff, etc) can then be estimated.</p>
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<p>Performance on a parallel machine can also be predicted from one-core
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or one-node timings if the parallel efficiency can be estimated. The
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communication bandwidth and latency of a particular parallel machine
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affects the efficiency. On most machines LAMMPS will give parallel
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efficiencies on these benchmarks above 50% so long as the number of
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atoms/core is a few 100 or greater, and closer to 100% for large
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numbers of atoms/core. This is for all-MPI mode with one MPI task per
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core. For nodes with accelerator options or hardware (OpenMP, GPU,
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Phi), you should first measure single node performance. Then you can
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estimate parallel performance for multi-node runs using the same logic
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as for all-MPI mode, except that now you will typically need many more
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atoms/node to achieve good scalability.</p>
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© Copyright 2013 Sandia Corporation.
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