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<CENTER><A HREF = "Section_example.html">Previous Section</A> - <A HREF = "http://lammps.sandia.gov">LAMMPS WWW Site</A> - <A HREF = "Manual.html">LAMMPS Documentation</A> - <A HREF = "Section_commands.html#comm">LAMMPS Commands</A> - <A HREF = "Section_tools.html">Next Section</A>
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<ul class="current">
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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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<HR>
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<H3>8. Performance & scalability
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</H3>
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<P>LAMMPS performance on several prototypical benchmarks and machines is
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discussed on the Benchmarks page of the <A HREF = "http://lammps.sandia.gov">LAMMPS WWW Site</A> where
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<li>8. Performance & scalability</li>
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<a href="Section_tools.html" class="btn btn-neutral float-right" title="9. Additional tools" accesskey="n">Next <span class="fa fa-arrow-circle-right"></span></a>
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<a href="Section_example.html" class="btn btn-neutral" title="7. Example problems" accesskey="p"><span class="fa fa-arrow-circle-left"></span> Previous</a>
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<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
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<div itemprop="articleBody">
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<div class="section" id="performance-scalability">
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<h1>8. Performance & scalability<a class="headerlink" href="#performance-scalability" title="Permalink to this headline">¶</a></h1>
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<p>LAMMPS performance on several prototypical benchmarks and machines is
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discussed on the Benchmarks page of the <a class="reference external" href="http://lammps.sandia.gov">LAMMPS WWW Site</a> where
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CPU timings and parallel efficiencies are listed. Here, the
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benchmarks are described briefly and some useful rules of thumb about
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their performance are highlighted.
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</P>
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<P>These are the 5 benchmark problems:
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</P>
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<OL><LI>LJ = atomic fluid, Lennard-Jones potential with 2.5 sigma cutoff (55
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neighbors per atom), NVE integration
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<LI>Chain = bead-spring polymer melt of 100-mer chains, FENE bonds and LJ
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their performance are highlighted.</p>
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<p>These are the 5 benchmark problems:</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</li>
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</ol>
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<blockquote>
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<div>neighbors per atom), NVE integration</div></blockquote>
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<ol class="arabic simple">
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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
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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
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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
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<LI>Rhodo = rhodopsin protein in solvated lipid bilayer, CHARMM force
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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
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</OL>
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<P>The input files for running the benchmarks are included in the LAMMPS
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integration</li>
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</ol>
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<p>The input files for running the benchmarks are included in the LAMMPS
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distribution, as are sample output files. Each of the 5 problems has
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32,000 atoms and runs for 100 timesteps. Each can be run as a serial
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benchmarks (on one processor) or in parallel. In parallel, each
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@ -46,30 +170,54 @@ fixed-size benchmarking, the same 32K atom problem is run on various
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numbers of processors. For scaled-size benchmarking, the model size
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is increased with the number of processors. E.g. on 8 processors, a
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256K-atom problem is run; on 1024 processors, a 32-million atom
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problem is run, etc.
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</P>
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<P>A useful metric from the benchmarks is the CPU cost per atom per
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problem is run, etc.</p>
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<p>A useful metric from the benchmarks is the CPU cost per atom per
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timestep. Since LAMMPS performance scales roughly linearly with
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problem size and timesteps, the run time of any problem using the same
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model (atom style, force field, cutoff, etc) can then be estimated.
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For example, on a 1.7 GHz Pentium desktop machine (Intel icc compiler
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under Red Hat Linux), the CPU run-time in seconds/atom/timestep for
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the 5 problems is
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</P>
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<DIV ALIGN=center><TABLE BORDER=1 >
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<TR ALIGN="center"><TD ALIGN ="right">Problem:</TD><TD > LJ</TD><TD > Chain</TD><TD > EAM</TD><TD > Chute</TD><TD > Rhodopsin</TD></TR>
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<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>
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<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
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</TD></TR></TABLE></DIV>
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<P>The ratios mean that if the atomic LJ system has a normalized cost of
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the 5 problems is</p>
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<table border="1" class="docutils">
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<colgroup>
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<col width="25%" />
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<col width="14%" />
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<col width="14%" />
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<col width="14%" />
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<col width="14%" />
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<col width="17%" />
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</colgroup>
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<tbody valign="top">
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<tr class="row-odd"><td>Problem:</td>
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<td>LJ</td>
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<td>Chain</td>
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<td>EAM</td>
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<td>Chute</td>
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<td>Rhodopsin</td>
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</tr>
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<tr class="row-even"><td>CPU/atom/step:</td>
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<td>4.55E-6</td>
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<td>2.18E-6</td>
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<td>9.38E-6</td>
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<td>2.18E-6</td>
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<td>1.11E-4</td>
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</tr>
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<tr class="row-odd"><td>Ratio to LJ:</td>
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<td>1.0</td>
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<td>0.48</td>
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<td>2.06</td>
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<td>0.48</td>
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<td>24.5</td>
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</tr>
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</tbody>
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</table>
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<p>The ratios mean that if the atomic LJ system has a normalized cost of
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1.0, the bead-spring chains and granular systems run 2x faster, while
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the EAM metal and solvated protein models run 2x and 25x slower
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respectively. The bulk of these cost differences is due to the
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expense of computing a particular pairwise force field for a given
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number of neighbors per atom.
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</P>
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<P>Performance on a parallel machine can also be predicted from the
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number of neighbors per atom.</p>
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<p>Performance on a parallel machine can also be predicted from the
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one-processor timings if the parallel efficiency can be estimated.
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The communication bandwidth and latency of a particular parallel
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machine affects the efficiency. On most machines LAMMPS will give
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@ -77,8 +225,80 @@ fixed-size parallel efficiencies on these benchmarks above 50% so long
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as the atoms/processor count is a few 100 or greater - i.e. on 64 to
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128 processors. Likewise, scaled-size parallel efficiencies will
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typically be 80% or greater up to very large processor counts. The
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benchmark data on the <A HREF = "http://lammps.sandia.gov">LAMMPS WWW Site</A> gives specific examples on
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benchmark data on the <a class="reference external" href="http://lammps.sandia.gov">LAMMPS WWW Site</a> gives specific examples on
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some different machines, including a run of 3/4 of a billion LJ atoms
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on 1500 processors that ran at 85% parallel efficiency.
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</P>
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</HTML>
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on 1500 processors that ran at 85% parallel efficiency.</p>
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