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<div class="lammps_version">Version: <b>19 Nov 2024</b></div>
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<li class="toctree-l1"><a class="reference internal" href="Intro.html">1. Introduction</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Install.html">2. Install LAMMPS</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Build.html">3. Build LAMMPS</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Run_head.html">4. Run LAMMPS</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Commands.html">5. Commands</a></li>
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<li class="toctree-l1 current"><a class="reference internal" href="Speed.html">7. Accelerate performance</a><ul class="current">
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<li class="toctree-l2 current"><a class="current reference internal" href="#">7.1. Benchmarks</a></li>
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<li class="toctree-l2"><a class="reference internal" href="Speed_measure.html">7.2. Measuring performance</a></li>
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<li class="toctree-l2"><a class="reference internal" href="Speed_tips.html">7.3. General tips</a></li>
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<li class="toctree-l2"><a class="reference internal" href="Speed_packages.html">7.4. Accelerator packages</a></li>
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<li class="toctree-l2"><a class="reference internal" href="Speed_compare.html">7.5. Comparison of various accelerator packages</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Library.html">1. LAMMPS Library Interfaces</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Modify.html">3. Modifying & extending LAMMPS</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Developer.html">4. Information for Developers</a></li>
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<p class="caption" role="heading"><span class="caption-text">Command Reference</span></p>
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<li class="breadcrumb-item"><a href="Speed.html"><span class="section-number">7. </span>Accelerate performance</a></li>
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<p><span class="math notranslate nohighlight">\(\renewcommand{\AA}{\text{Å}}\)</span></p>
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<section id="benchmarks">
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<h1><span class="section-number">7.1. </span>Benchmarks<a class="headerlink" href="#benchmarks" title="Link to this heading"></a></h1>
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<p>Current LAMMPS performance is discussed on the <a class="reference external" href="https://www.lammps.org/bench.html">Benchmarks page</a> of the <a class="reference external" href="https://www.lammps.org">LAMMPS website</a>
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where timings and parallel efficiency are listed. The page has
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several sections, which are briefly described below:</p>
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<ul class="simple">
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<li><p>CPU performance on 5 standard problems, strong and weak scaling</p></li>
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<li><p>GPU and Xeon Phi performance on same and related problems</p></li>
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<li><p>Comparison of cost of interatomic potentials</p></li>
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<li><p>Performance of huge, billion-atom problems</p></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><p>LJ = atomic fluid, Lennard-Jones potential with 2.5 sigma cutoff (55
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neighbors per atom), NVE integration</p></li>
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<li><p>Chain = bead-spring polymer melt of 100-mer chains, FENE bonds and LJ
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pairwise interactions with a <span class="math notranslate nohighlight">\(2^{\frac{1}{6}}\)</span> sigma cutoff (5 neighbors per
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atom), NVE integration</p></li>
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<li><p>EAM = metallic solid, Cu EAM potential with 4.95 Angstrom cutoff (45
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neighbors per atom), NVE integration</p></li>
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<li><p>Chute = granular chute flow, frictional history potential with 1.1
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sigma cutoff (7 neighbors per atom), NVE integration</p></li>
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<li><p>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</p></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 <code class="docutils literal notranslate"><span class="pre">bench/README</span></code> 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 <code class="docutils literal notranslate"><span class="pre">log.*</span></code> files that show performance
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of these 5 problems on 1 or 4 cores of Linux desktop. The <code class="docutils literal notranslate"><span class="pre">bench/FERMI</span></code>
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and <code class="docutils literal notranslate"><span class="pre">bench/KEPLER</span></code> directories 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 <code class="docutils literal notranslate"><span class="pre">README</span></code> files in those directories and the
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<a class="reference internal" href="Speed_packages.html"><span class="doc">Accelerator packages</span></a> pages for instructions on how
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to build LAMMPS and run on that kind of hardware.</p>
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<p>The <code class="docutils literal notranslate"><span class="pre">bench/POTENTIALS</span></code> directory has input files which correspond to the
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table of results on the
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<a class="reference external" href="https://www.lammps.org/bench.html#potentials">Potentials</a> 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 <a class="reference external" href="https://www.lammps.org/bench.html#billion">billion-atom</a> 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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<code class="docutils literal notranslate"><span class="pre">bench/in.lj</span></code> 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. interatomic 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 a parallel
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efficiency 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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</section>
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