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<li class="toctree-l1"><a class="reference internal" href="Section_intro.html">1. Introduction</a></li>
<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_packages.html">4. Packages</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="">5. Accelerating LAMMPS performance</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#measuring-performance">5.1. Measuring performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="#general-strategies">5.2. General strategies</a></li>
<li class="toctree-l2"><a class="reference internal" href="#packages-with-optimized-styles">5.3. Packages with optimized styles</a></li>
<li class="toctree-l2"><a class="reference internal" href="#comparison-of-various-accelerator-packages">5.4. Comparison of various accelerator packages</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#examples">5.4.1. Examples</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Section_perf.html">8. Performance &amp; scalability</a></li>
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<div class="section" id="accelerating-lammps-performance">
<h1>5. Accelerating LAMMPS performance<a class="headerlink" href="#accelerating-lammps-performance" title="Permalink to this headline"></a></h1>
<p>This section describes various methods for improving LAMMPS
performance for different classes of problems running on different
kinds of machines.</p>
<p>There are two thrusts to the discussion that follows. The
first is using code options that implement alternate algorithms
that can speed-up a simulation. The second is to use one
of the several accelerator packages provided with LAMMPS that
contain code optimized for certain kinds of hardware, including
multi-core CPUs, GPUs, and Intel Xeon Phi coprocessors.</p>
<ul class="simple">
<li>5.1 <a class="reference internal" href="#acc-1"><span>Measuring performance</span></a></li>
<li>5.2 <a class="reference internal" href="#acc-2"><span>Algorithms and code options to boost performace</span></a></li>
<li>5.3 <a class="reference internal" href="#acc-3"><span>Accelerator packages with optimized styles</span></a></li>
<li>5.3.1 <a class="reference internal" href="accelerate_cuda.html"><em>USER-CUDA package</em></a></li>
<li>5.3.2 <a class="reference internal" href="accelerate_gpu.html"><em>GPU package</em></a></li>
<li>5.3.3 <a class="reference internal" href="accelerate_intel.html"><em>USER-INTEL package</em></a></li>
<li>5.3.4 <a class="reference internal" href="accelerate_kokkos.html"><em>KOKKOS package</em></a></li>
<li>5.3.5 <a class="reference internal" href="accelerate_omp.html"><em>USER-OMP package</em></a></li>
<li>5.3.6 <a class="reference internal" href="accelerate_opt.html"><em>OPT package</em></a></li>
<li>5.4 <a class="reference internal" href="#acc-4"><span>Comparison of various accelerator packages</span></a></li>
</ul>
<p>The <a class="reference external" href="http://lammps.sandia.gov/bench.html">Benchmark page</a> of the LAMMPS
web site gives performance results for the various accelerator
packages discussed in Section 5.2, for several of the standard LAMMPS
benchmark problems, as a function of problem size and number of
compute nodes, on different hardware platforms.</p>
<div class="section" id="measuring-performance">
<span id="acc-1"></span><h2>5.1. Measuring performance<a class="headerlink" href="#measuring-performance" title="Permalink to this headline"></a></h2>
<p>Before trying to make your simulation run faster, you should
understand how it currently performs and where the bottlenecks are.</p>
<p>The best way to do this is run the your system (actual number of
atoms) for a modest number of timesteps (say 100 steps) on several
different processor counts, including a single processor if possible.
Do this for an equilibrium version of your system, so that the
100-step timings are representative of a much longer run. There is
typically no need to run for 1000s of timesteps to get accurate
timings; you can simply extrapolate from short runs.</p>
<p>For the set of runs, look at the timing data printed to the screen and
log file at the end of each LAMMPS run. <a class="reference internal" href="Section_start.html#start-8"><span>This section</span></a> of the manual has an overview.</p>
<p>Running on one (or a few processors) should give a good estimate of
the serial performance and what portions of the timestep are taking
the most time. Running the same problem on a few different processor
counts should give an estimate of parallel scalability. I.e. if the
simulation runs 16x faster on 16 processors, its 100% parallel
efficient; if it runs 8x faster on 16 processors, it&#8217;s 50% efficient.</p>
<p>The most important data to look at in the timing info is the timing
breakdown and relative percentages. For example, trying different
options for speeding up the long-range solvers will have little impact
if they only consume 10% of the run time. If the pairwise time is
dominating, you may want to look at GPU or OMP versions of the pair
style, as discussed below. Comparing how the percentages change as
you increase the processor count gives you a sense of how different
operations within the timestep are scaling. Note that if you are
running with a Kspace solver, there is additional output on the
breakdown of the Kspace time. For PPPM, this includes the fraction
spent on FFTs, which can be communication intensive.</p>
<p>Another important detail in the timing info are the histograms of
atoms counts and neighbor counts. If these vary widely across
processors, you have a load-imbalance issue. This often results in
inaccurate relative timing data, because processors have to wait when
communication occurs for other processors to catch up. Thus the
reported times for &#8220;Communication&#8221; or &#8220;Other&#8221; may be higher than they
really are, due to load-imbalance. If this is an issue, you can
uncomment the MPI_Barrier() lines in src/timer.cpp, and recompile
LAMMPS, to obtain synchronized timings.</p>
<hr class="docutils" />
</div>
<div class="section" id="general-strategies">
<span id="acc-2"></span><h2>5.2. General strategies<a class="headerlink" href="#general-strategies" title="Permalink to this headline"></a></h2>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">this section 5.2 is still a work in progress</p>
</div>
<p>Here is a list of general ideas for improving simulation performance.
Most of them are only applicable to certain models and certain
bottlenecks in the current performance, so let the timing data you
generate be your guide. It is hard, if not impossible, to predict how
much difference these options will make, since it is a function of
problem size, number of processors used, and your machine. There is
no substitute for identifying performance bottlenecks, and trying out
various options.</p>
<ul class="simple">
<li>rRESPA</li>
<li>2-FFT PPPM</li>
<li>Staggered PPPM</li>
<li>single vs double PPPM</li>
<li>partial charge PPPM</li>
<li>verlet/split run style</li>
<li>processor command for proc layout and numa layout</li>
<li>load-balancing: balance and fix balance</li>
</ul>
<p>2-FFT PPPM, also called <em>analytic differentiation</em> or <em>ad</em> PPPM, uses
2 FFTs instead of the 4 FFTs used by the default <em>ik differentiation</em>
PPPM. However, 2-FFT PPPM also requires a slightly larger mesh size to
achieve the same accuracy as 4-FFT PPPM. For problems where the FFT
cost is the performance bottleneck (typically large problems running
on many processors), 2-FFT PPPM may be faster than 4-FFT PPPM.</p>
<p>Staggered PPPM performs calculations using two different meshes, one
shifted slightly with respect to the other. This can reduce force
aliasing errors and increase the accuracy of the method, but also
doubles the amount of work required. For high relative accuracy, using
staggered PPPM allows one to half the mesh size in each dimension as
compared to regular PPPM, which can give around a 4x speedup in the
kspace time. However, for low relative accuracy, using staggered PPPM
gives little benefit and can be up to 2x slower in the kspace
time. For example, the rhodopsin benchmark was run on a single
processor, and results for kspace time vs. relative accuracy for the
different methods are shown in the figure below. For this system,
staggered PPPM (using ik differentiation) becomes useful when using a
relative accuracy of slightly greater than 1e-5 and above.</p>
<img alt="_images/rhodo_staggered.jpg" class="align-center" src="_images/rhodo_staggered.jpg" />
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">Using staggered PPPM may not give the same increase in
accuracy of energy and pressure as it does in forces, so some caution
must be used if energy and/or pressure are quantities of interest,
such as when using a barostat.</p>
</div>
<hr class="docutils" />
</div>
<div class="section" id="packages-with-optimized-styles">
<span id="acc-3"></span><h2>5.3. Packages with optimized styles<a class="headerlink" href="#packages-with-optimized-styles" title="Permalink to this headline"></a></h2>
<p>Accelerated versions of various <a class="reference internal" href="pair_style.html"><em>pair_style</em></a>,
<a class="reference internal" href="fix.html"><em>fixes</em></a>, <a class="reference internal" href="compute.html"><em>computes</em></a>, and other commands have
been added to LAMMPS, which will typically run faster than the
standard non-accelerated versions. Some require appropriate hardware
to be present on your system, e.g. GPUs or Intel Xeon Phi
coprocessors.</p>
<p>All of these commands are in packages provided with LAMMPS. An
overview of packages is give in <a class="reference internal" href="Section_packages.html"><em>Section packages</em></a>. These are the accelerator packages
currently in LAMMPS, either as standard or user packages:</p>
<table border="1" class="docutils">
<colgroup>
<col width="44%" />
<col width="56%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td><a class="reference internal" href="accelerate_cuda.html"><em>USER-CUDA</em></a></td>
<td>for NVIDIA GPUs</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="accelerate_gpu.html"><em>GPU</em></a></td>
<td>for NVIDIA GPUs as well as OpenCL support</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="accelerate_intel.html"><em>USER-INTEL</em></a></td>
<td>for Intel CPUs and Intel Xeon Phi</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="accelerate_kokkos.html"><em>KOKKOS</em></a></td>
<td>for GPUs, Intel Xeon Phi, and OpenMP threading</td>
</tr>
<tr class="row-odd"><td><a class="reference internal" href="accelerate_omp.html"><em>USER-OMP</em></a></td>
<td>for OpenMP threading</td>
</tr>
<tr class="row-even"><td><a class="reference internal" href="accelerate_opt.html"><em>OPT</em></a></td>
<td>generic CPU optimizations</td>
</tr>
</tbody>
</table>
<p>Any accelerated style has the same name as the corresponding standard
style, except that a suffix is appended. Otherwise, the syntax for
the command that uses the style is identical, their functionality is
the same, and the numerical results it produces should also be the
same, except for precision and round-off effects.</p>
<p>For example, all of these styles are accelerated variants of the
Lennard-Jones <a class="reference internal" href="pair_lj.html"><em>pair_style lj/cut</em></a>:</p>
<ul class="simple">
<li><a class="reference internal" href="pair_lj.html"><em>pair_style lj/cut/cuda</em></a></li>
<li><a class="reference internal" href="pair_lj.html"><em>pair_style lj/cut/gpu</em></a></li>
<li><a class="reference internal" href="pair_lj.html"><em>pair_style lj/cut/intel</em></a></li>
<li><a class="reference internal" href="pair_lj.html"><em>pair_style lj/cut/kk</em></a></li>
<li><a class="reference internal" href="pair_lj.html"><em>pair_style lj/cut/omp</em></a></li>
<li><a class="reference internal" href="pair_lj.html"><em>pair_style lj/cut/opt</em></a></li>
</ul>
<p>To see what accelerate styles are currently available, see
<a class="reference internal" href="Section_commands.html#cmd-5"><span>Section_commands 5</span></a> of the manual. The
doc pages for individual commands (e.g. <a class="reference internal" href="pair_lj.html"><em>pair lj/cut</em></a> or
<a class="reference internal" href="fix_nve.html"><em>fix nve</em></a>) also list any accelerated variants available
for that style.</p>
<p>To use an accelerator package in LAMMPS, and one or more of the styles
it provides, follow these general steps. Details vary from package to
package and are explained in the individual accelerator doc pages,
listed above:</p>
<table border="1" class="docutils">
<colgroup>
<col width="26%" />
<col width="74%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td>build the accelerator library</td>
<td>only for USER-CUDA and GPU packages</td>
</tr>
<tr class="row-even"><td>install the accelerator package</td>
<td>make yes-opt, make yes-user-intel, etc</td>
</tr>
</tbody>
</table>
<div class="line-block">
<div class="line">install the accelerator package | make yes-opt, make yes-user-intel, etc |</div>
</div>
<blockquote>
<div>only for USER-INTEL, KOKKOS, USER-OMP packages |</div></blockquote>
<table border="1" class="docutils">
<colgroup>
<col width="26%" />
<col width="74%" />
</colgroup>
<tbody valign="top">
<tr class="row-odd"><td>re-build LAMMPS</td>
<td>make machine</td>
</tr>
<tr class="row-even"><td>run a LAMMPS simulation</td>
<td>lmp_machine &lt; in.script</td>
</tr>
</tbody>
</table>
<div class="line-block">
<div class="line">run a LAMMPS simulation | lmp_machine &lt; in.script |</div>
</div>
<blockquote>
<div>only for USER-CUDA and KOKKOS packages |</div></blockquote>
<blockquote>
<div><a class="reference internal" href="package.html"><em>package</em></a> command, &lt;br&gt;
only if defaults need to be changed |</div></blockquote>
<blockquote>
<div><a class="reference internal" href="suffix.html"><em>suffix</em></a> command |</div></blockquote>
<table border="1" class="docutils">
<colgroup>
</colgroup>
<tbody valign="top">
</tbody>
</table>
<p>The first 4 steps can be done as a single command, using the
src/Make.py tool. The Make.py tool is discussed in <a class="reference internal" href="Section_start.html#start-4"><span>Section 2.4</span></a> of the manual, and its use is
illustrated in the individual accelerator sections. Typically these
steps only need to be done once, to create an executable that uses one
or more accelerator packages.</p>
<p>The last 4 steps can all be done from the command-line when LAMMPS is
launched, without changing your input script, as illustrated in the
individual accelerator sections. Or you can add
<a class="reference internal" href="package.html"><em>package</em></a> and <a class="reference internal" href="suffix.html"><em>suffix</em></a> commands to your input
script.</p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">With a few exceptions, you can build a single LAMMPS
executable with all its accelerator packages installed. Note that the
USER-INTEL and KOKKOS packages require you to choose one of their
options when building. I.e. CPU or Phi for USER-INTEL. OpenMP, Cuda,
or Phi for KOKKOS. Here are the exceptions; you cannot build a single
executable with:</p>
</div>
<ul class="simple">
<li>both the USER-INTEL Phi and KOKKOS Phi options</li>
<li>the USER-INTEL Phi or Kokkos Phi option, and either the USER-CUDA or GPU packages</li>
</ul>
<p>See the examples/accelerate/README and make.list files for sample
Make.py commands that build LAMMPS with any or all of the accelerator
packages. As an example, here is a command that builds with all the
GPU related packages installed (USER-CUDA, GPU, KOKKOS with Cuda),
including settings to build the needed auxiliary USER-CUDA and GPU
libraries for Kepler GPUs:</p>
<pre class="literal-block">
Make.py -j 16 -p omp gpu cuda kokkos -cc nvcc wrap=mpi -cuda mode=double arch=35 -gpu mode=double arch=35 -kokkos cuda arch=35 lib-all file mpi
</pre>
<p>The examples/accelerate directory also has input scripts that can be
used with all of the accelerator packages. See its README file for
details.</p>
<p>Likewise, the bench directory has FERMI and KEPLER and PHI
sub-directories with Make.py commands and input scripts for using all
the accelerator packages on various machines. See the README files in
those dirs.</p>
<p>As mentioned above, the <a class="reference external" href="http://lammps.sandia.gov/bench.html">Benchmark page</a> of the LAMMPS web site gives
performance results for the various accelerator packages for several
of the standard LAMMPS benchmark problems, as a function of problem
size and number of compute nodes, on different hardware platforms.</p>
<p>Here is a brief summary of what the various packages provide. Details
are in the individual accelerator sections.</p>
<ul class="simple">
<li>Styles with a &#8220;cuda&#8221; or &#8220;gpu&#8221; suffix are part of the USER-CUDA or GPU
packages, and can be run on NVIDIA GPUs. The speed-up on a GPU
depends on a variety of factors, discussed in the accelerator
sections.</li>
<li>Styles with an &#8220;intel&#8221; suffix are part of the USER-INTEL
package. These styles support vectorized single and mixed precision
calculations, in addition to full double precision. In extreme cases,
this can provide speedups over 3.5x on CPUs. The package also
supports acceleration in &#8220;offload&#8221; mode to Intel(R) Xeon Phi(TM)
coprocessors. This can result in additional speedup over 2x depending
on the hardware configuration.</li>
<li>Styles with a &#8220;kk&#8221; suffix are part of the KOKKOS package, and can be
run using OpenMP on multicore CPUs, on an NVIDIA GPU, or on an Intel
Xeon Phi in &#8220;native&#8221; mode. The speed-up depends on a variety of
factors, as discussed on the KOKKOS accelerator page.</li>
<li>Styles with an &#8220;omp&#8221; suffix are part of the USER-OMP package and allow
a pair-style to be run in multi-threaded mode using OpenMP. This can
be useful on nodes with high-core counts when using less MPI processes
than cores is advantageous, e.g. when running with PPPM so that FFTs
are run on fewer MPI processors or when the many MPI tasks would
overload the available bandwidth for communication.</li>
<li>Styles with an &#8220;opt&#8221; suffix are part of the OPT package and typically
speed-up the pairwise calculations of your simulation by 5-25% on a
CPU.</li>
</ul>
<p>The individual accelerator package doc pages explain:</p>
<ul class="simple">
<li>what hardware and software the accelerated package requires</li>
<li>how to build LAMMPS with the accelerated package</li>
<li>how to run with the accelerated package either via command-line switches or modifying the input script</li>
<li>speed-ups to expect</li>
<li>guidelines for best performance</li>
<li>restrictions</li>
</ul>
<hr class="docutils" />
</div>
<div class="section" id="comparison-of-various-accelerator-packages">
<span id="acc-4"></span><h2>5.4. Comparison of various accelerator packages<a class="headerlink" href="#comparison-of-various-accelerator-packages" title="Permalink to this headline"></a></h2>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">this section still needs to be re-worked with additional KOKKOS
and USER-INTEL information.</p>
</div>
<p>The next section compares and contrasts the various accelerator
options, since there are multiple ways to perform OpenMP threading,
run on GPUs, and run on Intel Xeon Phi coprocessors.</p>
<p>All 3 of these packages accelerate a LAMMPS calculation using NVIDIA
hardware, but they do it in different ways.</p>
<p>As a consequence, for a particular simulation on specific hardware,
one package may be faster than the other. We give guidelines below,
but the best way to determine which package is faster for your input
script is to try both of them on your machine. See the benchmarking
section below for examples where this has been done.</p>
<p><strong>Guidelines for using each package optimally:</strong></p>
<ul class="simple">
<li>The GPU package allows you to assign multiple CPUs (cores) to a single
GPU (a common configuration for &#8220;hybrid&#8221; nodes that contain multicore
CPU(s) and GPU(s)) and works effectively in this mode. The USER-CUDA
package does not allow this; you can only use one CPU per GPU.</li>
<li>The GPU package moves per-atom data (coordinates, forces)
back-and-forth between the CPU and GPU every timestep. The USER-CUDA
package only does this on timesteps when a CPU calculation is required
(e.g. to invoke a fix or compute that is non-GPU-ized). Hence, if you
can formulate your input script to only use GPU-ized fixes and
computes, and avoid doing I/O too often (thermo output, dump file
snapshots, restart files), then the data transfer cost of the
USER-CUDA package can be very low, causing it to run faster than the
GPU package.</li>
<li>The GPU package is often faster than the USER-CUDA package, if the
number of atoms per GPU is &#8220;small&#8221;. The crossover point, in terms of
atoms/GPU at which the USER-CUDA package becomes faster depends
strongly on the pair style. For example, for a simple Lennard Jones
system the crossover (in single precision) is often about 50K-100K
atoms per GPU. When performing double precision calculations the
crossover point can be significantly smaller.</li>
<li>Both packages compute bonded interactions (bonds, angles, etc) on the
CPU. This means a model with bonds will force the USER-CUDA package
to transfer per-atom data back-and-forth between the CPU and GPU every
timestep. If the GPU package is running with several MPI processes
assigned to one GPU, the cost of computing the bonded interactions is
spread across more CPUs and hence the GPU package can run faster.</li>
<li>When using the GPU package with multiple CPUs assigned to one GPU, its
performance depends to some extent on high bandwidth between the CPUs
and the GPU. Hence its performance is affected if full 16 PCIe lanes
are not available for each GPU. In HPC environments this can be the
case if S2050/70 servers are used, where two devices generally share
one PCIe 2.0 16x slot. Also many multi-GPU mainboards do not provide
full 16 lanes to each of the PCIe 2.0 16x slots.</li>
</ul>
<p><strong>Differences between the two packages:</strong></p>
<ul class="simple">
<li>The GPU package accelerates only pair force, neighbor list, and PPPM
calculations. The USER-CUDA package currently supports a wider range
of pair styles and can also accelerate many fix styles and some
compute styles, as well as neighbor list and PPPM calculations.</li>
<li>The USER-CUDA package does not support acceleration for minimization.</li>
<li>The USER-CUDA package does not support hybrid pair styles.</li>
<li>The USER-CUDA package can order atoms in the neighbor list differently
from run to run resulting in a different order for force accumulation.</li>
<li>The USER-CUDA package has a limit on the number of atom types that can be
used in a simulation.</li>
<li>The GPU package requires neighbor lists to be built on the CPU when using
exclusion lists or a triclinic simulation box.</li>
<li>The GPU package uses more GPU memory than the USER-CUDA package. This
is generally not a problem since typical runs are computation-limited
rather than memory-limited.</li>
</ul>
<div class="section" id="examples">
<h3>5.4.1. Examples<a class="headerlink" href="#examples" title="Permalink to this headline"></a></h3>
<p>The LAMMPS distribution has two directories with sample input scripts
for the GPU and USER-CUDA packages.</p>
<ul class="simple">
<li>lammps/examples/gpu = GPU package files</li>
<li>lammps/examples/USER/cuda = USER-CUDA package files</li>
</ul>
<p>These contain input scripts for identical systems, so they can be used
to benchmark the performance of both packages on your system.</p>
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