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<title>5.USER-CUDA package — LAMMPS 15 May 2015 version documentation</title>
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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"><a class="reference internal" href="Section_perf.html">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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<p><a class="reference internal" href="Section_accelerate.html"><em>Return to Section accelerate overview</em></a></p>
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<div class="section" id="user-cuda-package">
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<h1>5.USER-CUDA package<a class="headerlink" href="#user-cuda-package" title="Permalink to this headline">¶</a></h1>
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<p>The USER-CUDA package was developed by Christian Trott (Sandia) while
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at U Technology Ilmenau in Germany. It provides NVIDIA GPU versions
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of many pair styles, many fixes, a few computes, and for long-range
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Coulombics via the PPPM command. It has the following general
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features:</p>
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<ul class="simple">
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<li>The package is designed to allow an entire LAMMPS calculation, for
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many timesteps, to run entirely on the GPU (except for inter-processor
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MPI communication), so that atom-based data (e.g. coordinates, forces)
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do not have to move back-and-forth between the CPU and GPU.</li>
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<li>The speed-up advantage of this approach is typically better when the
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number of atoms per GPU is large</li>
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<li>Data will stay on the GPU until a timestep where a non-USER-CUDA fix
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or compute is invoked. Whenever a non-GPU operation occurs (fix,
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compute, output), data automatically moves back to the CPU as needed.
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This may incur a performance penalty, but should otherwise work
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transparently.</li>
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<li>Neighbor lists are constructed on the GPU.</li>
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<li>The package only supports use of a single MPI task, running on a
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single CPU (core), assigned to each GPU.</li>
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</ul>
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<p>Here is a quick overview of how to use the USER-CUDA package:</p>
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<ul class="simple">
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<li>build the library in lib/cuda for your GPU hardware with desired precision</li>
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<li>include the USER-CUDA package and build LAMMPS</li>
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<li>use the mpirun command to specify 1 MPI task per GPU (on each node)</li>
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<li>enable the USER-CUDA package via the “-c on” command-line switch</li>
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<li>specify the # of GPUs per node</li>
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<li>use USER-CUDA styles in your input script</li>
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</ul>
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<p>The latter two steps can be done using the “-pk cuda” and “-sf cuda”
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<a class="reference internal" href="Section_start.html#start-7"><span>command-line switches</span></a> respectively. Or
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the effect of the “-pk” or “-sf” switches can be duplicated by adding
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the <a class="reference internal" href="package.html"><em>package cuda</em></a> or <a class="reference internal" href="suffix.html"><em>suffix cuda</em></a> commands
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respectively to your input script.</p>
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<p><strong>Required hardware/software:</strong></p>
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<p>To use this package, you need to have one or more NVIDIA GPUs and
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install the NVIDIA Cuda software on your system:</p>
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<p>Your NVIDIA GPU needs to support Compute Capability 1.3. This list may
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help you to find out the Compute Capability of your card:</p>
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<p><a class="reference external" href="http://en.wikipedia.org/wiki/Comparison_of_Nvidia_graphics_processing_units">http://en.wikipedia.org/wiki/Comparison_of_Nvidia_graphics_processing_units</a></p>
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<p>Install the Nvidia Cuda Toolkit (version 3.2 or higher) and the
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corresponding GPU drivers. The Nvidia Cuda SDK is not required, but
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we recommend it also be installed. You can then make sure its sample
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projects can be compiled without problems.</p>
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<p><strong>Building LAMMPS with the USER-CUDA package:</strong></p>
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<p>This requires two steps (a,b): build the USER-CUDA library, then build
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LAMMPS with the USER-CUDA package.</p>
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<p>You can do both these steps in one line, using the src/Make.py script,
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described in <a class="reference internal" href="Section_start.html#start-4"><span>Section 2.4</span></a> of the manual.
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Type “Make.py -h” for help. If run from the src directory, this
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command will create src/lmp_cuda using src/MAKE/Makefile.mpi as the
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starting Makefile.machine:</p>
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<div class="highlight-python"><div class="highlight"><pre>Make.py -p cuda -cuda mode=single arch=20 -o cuda -a lib-cuda file mpi
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</pre></div>
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</div>
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<p>Or you can follow these two (a,b) steps:</p>
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<ol class="loweralpha simple">
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<li>Build the USER-CUDA library</li>
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</ol>
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<p>The USER-CUDA library is in lammps/lib/cuda. If your <em>CUDA</em> toolkit
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is not installed in the default system directoy <em>/usr/local/cuda</em> edit
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the file <em>lib/cuda/Makefile.common</em> accordingly.</p>
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<p>To build the library with the settings in lib/cuda/Makefile.default,
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simply type:</p>
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<div class="highlight-python"><div class="highlight"><pre><span class="n">make</span>
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</pre></div>
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</div>
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<p>To set options when the library is built, type “make OPTIONS”, where
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<em>OPTIONS</em> are one or more of the following. The settings will be
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written to the <em>lib/cuda/Makefile.defaults</em> before the build.</p>
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<pre class="literal-block">
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<em>precision=N</em> to set the precision level
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N = 1 for single precision (default)
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N = 2 for double precision
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N = 3 for positions in double precision
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N = 4 for positions and velocities in double precision
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<em>arch=M</em> to set GPU compute capability
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M = 35 for Kepler GPUs
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M = 20 for CC2.0 (GF100/110, e.g. C2050,GTX580,GTX470) (default)
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M = 21 for CC2.1 (GF104/114, e.g. GTX560, GTX460, GTX450)
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M = 13 for CC1.3 (GF200, e.g. C1060, GTX285)
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<em>prec_timer=0/1</em> to use hi-precision timers
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0 = do not use them (default)
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1 = use them
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this is usually only useful for Mac machines
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<em>dbg=0/1</em> to activate debug mode
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0 = no debug mode (default)
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1 = yes debug mode
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this is only useful for developers
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<em>cufft=1</em> for use of the CUDA FFT library
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0 = no CUFFT support (default)
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in the future other CUDA-enabled FFT libraries might be supported
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</pre>
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<p>If the build is successful, it will produce the files liblammpscuda.a and
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Makefile.lammps.</p>
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<p>Note that if you change any of the options (like precision), you need
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to re-build the entire library. Do a “make clean” first, followed by
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“make”.</p>
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<ol class="loweralpha simple" start="2">
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<li>Build LAMMPS with the USER-CUDA package</li>
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</ol>
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<div class="highlight-python"><div class="highlight"><pre>cd lammps/src
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make yes-user-cuda
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make machine
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</pre></div>
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</div>
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<p>No additional compile/link flags are needed in Makefile.machine.</p>
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<p>Note that if you change the USER-CUDA library precision (discussed
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above) and rebuild the USER-CUDA library, then you also need to
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re-install the USER-CUDA package and re-build LAMMPS, so that all
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affected files are re-compiled and linked to the new USER-CUDA
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library.</p>
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<p><strong>Run with the USER-CUDA package from the command line:</strong></p>
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<p>The mpirun or mpiexec command sets the total number of MPI tasks used
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by LAMMPS (one or multiple per compute node) and the number of MPI
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tasks used per node. E.g. the mpirun command in MPICH does this via
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its -np and -ppn switches. Ditto for OpenMPI via -np and -npernode.</p>
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<p>When using the USER-CUDA package, you must use exactly one MPI task
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per physical GPU.</p>
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<p>You must use the “-c on” <a class="reference internal" href="Section_start.html#start-7"><span>command-line switch</span></a> to enable the USER-CUDA package.
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The “-c on” switch also issues a default <a class="reference internal" href="package.html"><em>package cuda 1</em></a>
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command which sets various USER-CUDA options to default values, as
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discussed on the <a class="reference internal" href="package.html"><em>package</em></a> command doc page.</p>
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<p>Use the “-sf cuda” <a class="reference internal" href="Section_start.html#start-7"><span>command-line switch</span></a>,
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which will automatically append “cuda” to styles that support it. Use
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the “-pk cuda Ng” <a class="reference internal" href="Section_start.html#start-7"><span>command-line switch</span></a> to
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set Ng = # of GPUs per node to a different value than the default set
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by the “-c on” switch (1 GPU) or change other <a class="reference internal" href="package.html"><em>package cuda</em></a> options.</p>
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<div class="highlight-python"><div class="highlight"><pre>lmp_machine -c on -sf cuda -pk cuda 1 -in in.script # 1 MPI task uses 1 GPU
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mpirun -np 2 lmp_machine -c on -sf cuda -pk cuda 2 -in in.script # 2 MPI tasks use 2 GPUs on a single 16-core (or whatever) node
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mpirun -np 24 -ppn 2 lmp_machine -c on -sf cuda -pk cuda 2 -in in.script # ditto on 12 16-core nodes
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</pre></div>
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</div>
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<p>The syntax for the “-pk” switch is the same as same as the “package
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cuda” command. See the <a class="reference internal" href="package.html"><em>package</em></a> command doc page for
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details, including the default values used for all its options if it
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is not specified.</p>
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<p>Note that the default for the <a class="reference internal" href="package.html"><em>package cuda</em></a> command is
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to set the Newton flag to “off” for both pairwise and bonded
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interactions. This typically gives fastest performance. If the
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<a class="reference internal" href="newton.html"><em>newton</em></a> command is used in the input script, it can
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override these defaults.</p>
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<p><strong>Or run with the USER-CUDA package by editing an input script:</strong></p>
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<p>The discussion above for the mpirun/mpiexec command and the requirement
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of one MPI task per GPU is the same.</p>
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<p>You must still use the “-c on” <a class="reference internal" href="Section_start.html#start-7"><span>command-line switch</span></a> to enable the USER-CUDA package.</p>
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<p>Use the <a class="reference internal" href="suffix.html"><em>suffix cuda</em></a> command, or you can explicitly add a
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“cuda” suffix to individual styles in your input script, e.g.</p>
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<div class="highlight-python"><div class="highlight"><pre>pair_style lj/cut/cuda 2.5
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</pre></div>
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</div>
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<p>You only need to use the <a class="reference internal" href="package.html"><em>package cuda</em></a> command if you
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wish to change any of its option defaults, including the number of
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GPUs/node (default = 1), as set by the “-c on” <a class="reference internal" href="Section_start.html#start-7"><span>command-line switch</span></a>.</p>
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<p><strong>Speed-ups to expect:</strong></p>
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<p>The performance of a GPU versus a multi-core CPU is a function of your
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hardware, which pair style is used, the number of atoms/GPU, and the
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precision used on the GPU (double, single, mixed).</p>
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<p>See the <a class="reference external" href="http://lammps.sandia.gov/bench.html">Benchmark page</a> of the
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LAMMPS web site for performance of the USER-CUDA package on different
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hardware.</p>
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<p><strong>Guidelines for best performance:</strong></p>
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<ul class="simple">
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<li>The USER-CUDA package offers more speed-up relative to CPU performance
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when the number of atoms per GPU is large, e.g. on the order of tens
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or hundreds of 1000s.</li>
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<li>As noted above, this package will continue to run a simulation
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entirely on the GPU(s) (except for inter-processor MPI communication),
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for multiple timesteps, until a CPU calculation is required, either by
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a fix or compute that is non-GPU-ized, or until output is performed
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(thermo or dump snapshot or restart file). The less often this
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occurs, the faster your simulation will run.</li>
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</ul>
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<div class="section" id="restrictions">
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<h2>Restrictions<a class="headerlink" href="#restrictions" title="Permalink to this headline">¶</a></h2>
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<p>None.</p>
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