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5. Accelerating LAMMPS performance :h3

This section describes various methods for improving LAMMPS
performance for different classes of problems running on different
kinds of machines.

5.1 "Measuring performance"_#acc_1
5.2 "General strategies"_#acc_2
5.3 "Packages with optimized styles"_#acc_3
5.4 "OPT package"_#acc_4
5.5 "USER-OMP package"_#acc_5
5.6 "GPU package"_#acc_6
5.7 "USER-CUDA package"_#acc_7
5.8 "KOKKOS package"_#acc_8
5.9 "USER-INTEL package"_#acc_9
5.10 "Comparison of USER-CUDA, GPU, and KOKKOS packages"_#acc_10 :all(b)

The "Benchmark page"_http://lammps.sandia.gov/bench.html of the LAMMPS
web site gives performance results for the various accelerator
packages discussed in this section for several of the standard LAMMPS
benchmarks, as a function of problem size and number of compute nodes,
on different hardware platforms.

:line
:line

5.1 Measuring performance :h4,link(acc_1)

Before trying to make your simulation run faster, you should
understand how it currently performs and where the bottlenecks are.

The best way to do this is run the your system (actual number of
atoms) for a modest number of timesteps (say 100, or a few 100 at
most) 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 or timesteps
to get accurate timings; you can simply extrapolate from short runs.

For the set of runs, look at the timing data printed to the screen and
log file at the end of each LAMMPS run.  "This
section"_Section_start.html#start_8 of the manual has an overview.

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's 50% efficient.

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.

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 "Communication" or "Other" 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.

:line

5.2 General strategies :h4,link(acc_2)

NOTE: this section 5.2 is still a work in progress

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.

rRESPA
2-FFT PPPM
Staggered PPPM
single vs double PPPM
partial charge PPPM
verlet/split
processor mapping via processors numa command
load-balancing: balance and fix balance
processor command for layout
OMP when lots of cores :ul

2-FFT PPPM, also called {analytic differentiation} or {ad} PPPM, uses
2 FFTs instead of the 4 FFTs used by the default {ik differentiation}
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.
  
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.

:c,image(JPG/rhodo_staggered.jpg)

IMPORTANT NOTE: 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.

:line

5.3 Packages with optimized styles :h4,link(acc_3)

Accelerated versions of various "pair_style"_pair_style.html,
"fixes"_fix.html, "computes"_compute.html, and other commands have
been added to LAMMPS, which will typically run faster than the
standard non-accelerated versions, if you have the appropriate
hardware on your system.

All of these commands are in "packages"_Section_packages.html.
Currently, there are 6 such accelerator packages in LAMMPS, either as
standard or user packages:

"USER-CUDA"_#acc_7 : for NVIDIA GPUs
"GPU"_acc_6 : for NVIDIA GPUs as well as OpenCL support
"USER-INTEL"_acc_9 : for Intel CPUs and Intel Xeon Phi
"KOKKOS"_acc_8 : for GPUs, Intel Xeon Phi, and OpenMP threading
"USER-OMP"_acc_5 : for OpenMP threading
"OPT"_acc_4 : generic CPU optimizations :tb(s=:)

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 specifies 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.

For example, all of these styles are variants of the basic
Lennard-Jones "pair_style lj/cut"_pair_lj.html:

"pair_style lj/cut/cuda"_pair_lj.html
"pair_style lj/cut/gpu"_pair_lj.html
"pair_style lj/cut/intel"_pair_lj.html
"pair_style lj/cut/kk"_pair_lj.html
"pair_style lj/cut/omp"_pair_lj.html
"pair_style lj/cut/opt"_pair_lj.html :ul

Assuming LAMMPS was built with the appropriate package, these styles
can be invoked by specifying them explicitly in your input script.  Or
the "-suffix command-line switch"_Section_start.html#start_7 can be
used to automatically invoke the accelerated versions, without
changing the input script.  Use of the "suffix"_suffix.html command
allows a suffix to be set explicitly and to be turned off and back on
at various points within an input script.

To see what styles are currently available in each of the accelerated
packages, see "Section_commands 5"_Section_commands.html#cmd_5 of the
manual.  The doc page for each indvidual style (e.g. "pair
lj/cut"_pair_lj.html or "fix nve"_fix_nve.html) also lists any
accelerated variants available for that style.

Here is a brief summary of what the various packages provide.  Details
are in individual sections below.

Styles with a "cuda" or "gpu" suffix are part of the USER-CUDA or GPU
packages, and can be run on NVIDIA GPUs associated with your CPUs.
The speed-up on a GPU depends on a variety of factors, as discussed
below. :ulb,l

Styles with an "intel" 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 with offload to Intel(R) Xeon Phi(TM)
coprocessors.  This can result in additional speedup over 2x depending
on the hardware configuration. :l

Styles with a "kk" suffix are part of the KOKKOS package, and can be
run using OpenMP, on an NVIDIA GPU, or on an Intel(R) Xeon Phi(TM).
The speed-up depends on a variety of factors, as discussed below. :l

Styles with an "omp" 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. :l

Styles with an "opt" suffix are part of the OPT package and typically
speed-up the pairwise calculations of your simulation by 5-25% on a
CPU. :l,ule

The following sections explain:

what hardware and software the accelerated package requires
how to build LAMMPS with the accelerated package
how to run an input script with the accelerated package
speed-ups to expect
guidelines for best performance
restrictions :ul

The final section compares and contrasts the USER-CUDA, GPU, and
KOKKOS packages, since they all enable use of NVIDIA GPUs.

:line

5.4 OPT package :h4,link(acc_4)

The OPT package was developed by James Fischer (High Performance
Technologies), David Richie, and Vincent Natoli (Stone Ridge
Technologies).  It contains a handful of pair styles whose compute()
methods were rewritten in C++ templated form to reduce the overhead
due to if tests and other conditional code.

[Required hardware/software:]

None.

[Building LAMMPS with the OPT package:]

Include the package and build LAMMPS.

make yes-opt
make machine :pre

No additional compile/link flags are needed in your lo-level
src/MAKE/Makefile.machine.

[Running with the OPT package:]

You can explicitly add an "opt" suffix to the
"pair_style"_pair_style.html command in your input script:

pair_style lj/cut/opt 2.5 :pre

Or you can run with the -sf "command-line
switch"_Section_start.html#start_7, which will automatically append
"opt" to styles that support it.

lmp_machine -sf opt -in in.script
mpirun -np 4 lmp_machine -sf opt -in in.script :pre

[Speed-ups to expect:]

You should see a reduction in the "Pair time" value printed at the end
of a run.  On most machines for reasonable problem sizes, it will be a
5 to 20% savings.

[Guidelines for best performance:]

None.  Just try out an OPT pair style to see how it performs.

[Restrictions:]

None.

:line

5.5 USER-OMP package :h4,link(acc_5)

The USER-OMP package was developed by Axel Kohlmeyer at Temple
University.  It provides multi-threaded versions of most pair styles,
nearly all bonded styles (bond, angle, dihedral, improper), several
Kspace styles, and a few fix styles.  The package currently
uses the OpenMP interface for multi-threading.

[Required hardware/software:]

Your compiler must support the OpenMP interface.  You should have one
or more multi-core CPUs so that multiple threads can be launched by an
MPI task running on a CPU.

[Building LAMMPS with the USER-OMP package:]

Include the package and build LAMMPS.  

cd lammps/src
make yes-user-omp
make machine :pre

Your lo-level src/MAKE/Makefile.machine needs a flag for OpenMP
support in both the CCFLAGS and LINKFLAGS variables.  For GNU and
Intel compilers, this flag is {-fopenmp}.  Without this flag the
USER-OMP styles will still be compiled and work, but will not support
multi-threading.

[Running with the USER-OMP package:]

There are 3 issues (a,b,c) to address:

(a) Specify how many threads per MPI task to use

Note that the product of MPI tasks * threads/task should not exceed
the physical number of cores, otherwise performance will suffer.

By default LAMMPS uses 1 thread per MPI task.  If the environment
variable OMP_NUM_THREADS is set to a valid value, this value is used.
You can set this environment variable when you launch LAMMPS, e.g.

env OMP_NUM_THREADS=4 lmp_machine -sf omp -in in.script
env OMP_NUM_THREADS=2 mpirun -np 2 lmp_machine -sf omp -in in.script
mpirun -x OMP_NUM_THREADS=2 -np 2 lmp_machine -sf omp -in in.script :pre

or you can set it permanently in your shell's start-up script.  
All three of these examples use a total of 4 CPU cores.

Note that different MPI implementations have different ways of passing
the OMP_NUM_THREADS environment variable to all MPI processes.  The
2nd line above is for MPICH; the 3rd line with -x is for OpenMPI.
Check your MPI documentation for additional details.

You can also set the number of threads per MPI task via the "package
omp"_package.html command, which will override any OMP_NUM_THREADS
setting.

(b) Enable the USER-OMP package

This can be done in one of two ways.  Use a "package omp"_package.html
command near the top of your input script.

Or use the "-sf omp" "command-line switch"_Section_start.html#start_7,
which will automatically invoke the command "package omp
*"_package.html.

(c) Use OMP-accelerated styles

This can be done by explicitly adding an "omp" suffix to any supported
style in your input script:

pair_style lj/cut/omp 2.5
fix nve/omp :pre
<
Or you can run with the "-sf omp" "command-line
switch"_Section_start.html#start_7, which will automatically append
"omp" to styles that support it.

lmp_machine -sf omp -in in.script
mpirun -np 4 lmp_machine -sf omp -in in.script :pre

Using the "suffix omp" command in your input script does the same
thing.

[Speed-ups to expect:]

Depending on which styles are accelerated, you should look for a
reduction in the "Pair time", "Bond time", "KSpace time", and "Loop
time" values printed at the end of a run.  

You may see a small performance advantage (5 to 20%) when running a
USER-OMP style (in serial or parallel) with a single thread per MPI
task, versus running standard LAMMPS with its standard
(un-accelerated) styles (in serial or all-MPI parallelization with 1
task/core).  This is because many of the USER-OMP styles contain
similar optimizations to those used in the OPT package, as described
above.

With multiple threads/task, the optimal choice of MPI tasks/node and
OpenMP threads/task can vary a lot and should always be tested via
benchmark runs for a specific simulation running on a specific
machine, paying attention to guidelines discussed in the next
sub-section.

A description of the multi-threading strategy used in the UESR-OMP
package and some performance examples are "presented
here"_http://sites.google.com/site/akohlmey/software/lammps-icms/lammps-icms-tms2011-talk.pdf?attredirects=0&d=1

[Guidelines for best performance:]

For many problems on current generation CPUs, running the USER-OMP
package with a single thread/task is faster than running with multiple
threads/task.  This is because the MPI parallelization in LAMMPS is
often more efficient than multi-threading as implemented in the
USER-OMP package.  The parallel efficiency (in a threaded sense) also
varies for different USER-OMP styles.

Using multiple threads/task can be more effective under the following
circumstances:

Individual compute nodes have a significant number of CPU cores but
the CPU itself has limited memory bandwidth, e.g. for Intel Xeon 53xx
(Clovertown) and 54xx (Harpertown) quad core processors. Running one
MPI task per CPU core will result in significant performance
degradation, so that running with 4 or even only 2 MPI tasks per node
is faster.  Running in hybrid MPI+OpenMP mode will reduce the
inter-node communication bandwidth contention in the same way, but
offers an additional speedup by utilizing the otherwise idle CPU
cores. :ulb,l

The interconnect used for MPI communication does not provide
sufficient bandwidth for a large number of MPI tasks per node.  For
example, this applies to running over gigabit ethernet or on Cray XT4
or XT5 series supercomputers.  As in the aforementioned case, this
effect worsens when using an increasing number of nodes. :l

The system has a spatially inhomogeneous particle density which does
not map well to the "domain decomposition scheme"_processors.html or
"load-balancing"_balance.html options that LAMMPS provides.  This is
because multi-threading achives parallelism over the number of
particles, not via their distribution in space. :l

A machine is being used in "capability mode", i.e. near the point
where MPI parallelism is maxed out.  For example, this can happen when
using the "PPPM solver"_kspace_style.html for long-range
electrostatics on large numbers of nodes.  The scaling of the KSpace
calculation (see the "kspace_style"_kspace_style.html command) becomes
the performance-limiting factor.  Using multi-threading allows less
MPI tasks to be invoked and can speed-up the long-range solver, while
increasing overall performance by parallelizing the pairwise and
bonded calculations via OpenMP.  Likewise additional speedup can be
sometimes be achived by increasing the length of the Coulombic cutoff
and thus reducing the work done by the long-range solver.  Using the
"run_style verlet/split"_run_style.html command, which is compatible
with the USER-OMP package, is an alternative way to reduce the number
of MPI tasks assigned to the KSpace calculation. :l,ule

Other performance tips are as follows:

The best parallel efficiency from {omp} styles is typically achieved
when there is at least one MPI task per physical processor,
i.e. socket or die. :ulb,l

It is usually most efficient to restrict threading to a single
socket, i.e. use one or more MPI task per socket. :l

Several current MPI implementation by default use a processor affinity
setting that restricts each MPI task to a single CPU core.  Using
multi-threading in this mode will force the threads to share that core
and thus is likely to be counterproductive.  Instead, binding MPI
tasks to a (multi-core) socket, should solve this issue. :l,ule

[Restrictions:]

None.

:line

5.6 GPU package :h4,link(acc_6)

The GPU package was developed by Mike Brown at ORNL and his
collaborators, particularly Trung Nguyen (ORNL).  It provides GPU
versions of many pair styles, including the 3-body Stillinger-Weber
pair style, and for "kspace_style pppm"_kspace_style.html for
long-range Coulombics.  It has the following general features:

The package is designed to exploit common GPU hardware configurations
where one or more GPUs are coupled to many cores of one or more
multi-core CPUs, e.g. within a node of a parallel machine. :ulb,l

Atom-based data (e.g. coordinates, forces) moves back-and-forth
between the CPU(s) and GPU every timestep. :l

Neighbor lists can be constructed on the CPU or on the GPU :l

The charge assignement and force interpolation portions of PPPM can be
run on the GPU.  The FFT portion, which requires MPI communication
between processors, runs on the CPU. :l

Asynchronous force computations can be performed simultaneously on the
CPU(s) and GPU. :l

It allows for GPU computations to be performed in single or double
precision, or in mixed-mode precision, where pairwise forces are
computed in single precision, but accumulated into double-precision
force vectors. :l

LAMMPS-specific code is in the GPU package.  It makes calls to a
generic GPU library in the lib/gpu directory.  This library provides
NVIDIA support as well as more general OpenCL support, so that the
same functionality can eventually be supported on a variety of GPU
hardware. :l,ule

[Required hardware/software:]

To use this package, you currently need to have an NVIDIA GPU and
install the NVIDIA Cuda software on your system:

Check if you have an NVIDIA GPU: cat /proc/driver/nvidia/gpus/0/information
Go to http://www.nvidia.com/object/cuda_get.html
Install a driver and toolkit appropriate for your system (SDK is not necessary)
Run lammps/lib/gpu/nvc_get_devices (after building the GPU library, see below) to list supported devices and properties :ul

[Building LAMMPS with the GPU package:]

This requires two steps (a,b): build the GPU library, then build
LAMMPS.

(a) Build the GPU library

The GPU library is in lammps/lib/gpu.  Select a Makefile.machine (in
lib/gpu) appropriate for your system.  You should pay special
attention to 3 settings in this makefile.

CUDA_HOME = needs to be where NVIDIA Cuda software is installed on your system
CUDA_ARCH = needs to be appropriate to your GPUs
CUDA_PREC = precision (double, mixed, single) you desire :ul

See lib/gpu/Makefile.linux.double for examples of the ARCH settings
for different GPU choices, e.g. Fermi vs Kepler.  It also lists the
possible precision settings:

CUDA_PREC = -D_SINGLE_SINGLE  # Single precision for all calculations
CUDA_PREC = -D_DOUBLE_DOUBLE  # Double precision for all calculations
CUDA_PREC = -D_SINGLE_DOUBLE  # Accumulation of forces, etc, in double :pre

The last setting is the mixed mode referred to above.  Note that your
GPU must support double precision to use either the 2nd or 3rd of
these settings.

To build the library, type:

make -f Makefile.machine :pre

If successful, it will produce the files libgpu.a and Makefile.lammps.

The latter file has 3 settings that need to be appropriate for the
paths and settings for the CUDA system software on your machine.
Makefile.lammps is a copy of the file specified by the EXTRAMAKE
setting in Makefile.machine.  You can change EXTRAMAKE or create your
own Makefile.lammps.machine if needed.

Note that to change the precision of the GPU library, you need to
re-build the entire library.  Do a "clean" first, e.g. "make -f
Makefile.linux clean", followed by the make command above.

(b) Build LAMMPS

cd lammps/src
make yes-gpu
make machine :pre

Note that if you change the GPU library precision (discussed above),
you also need to re-install the GPU package and re-build LAMMPS, so
that all affected files are re-compiled and linked to the new GPU
library.

[Running with the GPU package:]

The examples/gpu and bench/GPU directories have scripts that can be
run with the GPU package, as well as detailed instructions on how to
run them.

To run with the GPU package, there are 3 basic issues (a,b,c) to
address:

(a) Use one or more MPI tasks per GPU

The total number of MPI tasks used by LAMMPS (one or multiple per
compute node) is set in the usual manner via the mpirun or mpiexec
commands, and is independent of the GPU package.

When using the GPU package, you cannot assign more than one physical
GPU to a single MPI task.  However multiple MPI tasks can share the
same GPU, and in many cases it will be more efficient to run this way.

The default is to have all MPI tasks on a compute node use a single
GPU.  To use multiple GPUs per node, be sure to create one or more MPI
tasks per GPU, and use the first/last settings in the "package
gpu"_package.html command to include all the GPU IDs on the node.
E.g. first = 0, last = 1, for 2 GPUs.  On a node with 8 CPU cores
and 2 GPUs, this would specify that each GPU is shared by 4 MPI tasks.

(b) Enable the GPU package

This can be done in one of two ways.  Use a "package gpu"_package.html
command near the top of your input script.

Or use the "-sf gpu" "command-line switch"_Section_start.html#start_7,
which will automatically invoke the command "package gpu force/neigh 0
0 1"_package.html.  Note that this specifies use of a single GPU (per
node), so you must specify the package command in your input script
explicitly if you want to use multiple GPUs per node.

(c) Use GPU-accelerated styles

This can be done by explicitly adding a "gpu" suffix to any supported
style in your input script:

pair_style lj/cut/gpu 2.5 :pre

Or you can run with the "-sf gpu" "command-line
switch"_Section_start.html#start_7, which will automatically append
"gpu" to styles that support it.

lmp_machine -sf gpu -in in.script
mpirun -np 4 lmp_machine -sf gpu -in in.script :pre

Using the "suffix gpu" command in your input script does the same
thing.

IMPORTANT NOTE: The input script must also use the
"newton"_newton.html command with a pairwise setting of {off},
since {on} is the default.

[Speed-ups to expect:]

The performance of a GPU versus a multi-core CPU is a function of your
hardware, which pair style is used, the number of atoms/GPU, and the
precision used on the GPU (double, single, mixed).

See the "Benchmark page"_http://lammps.sandia.gov/bench.html of the
LAMMPS web site for performance of the GPU package on various
hardware, including the Titan HPC platform at ORNL.

You should also experiment with how many MPI tasks per GPU to use to
give the best performance for your problem and machine.  This is also
a function of the problem size and the pair style being using.
Likewise, you should experiment with the precision setting for the GPU
library to see if single or mixed precision will give accurate
results, since they will typically be faster.

[Guidelines for best performance:]

Using multiple MPI tasks per GPU will often give the best performance,
as allowed my most multi-core CPU/GPU configurations. :ulb,l

If the number of particles per MPI task is small (e.g. 100s of
particles), it can be more efficient to run with fewer MPI tasks per
GPU, even if you do not use all the cores on the compute node. :l

The "package gpu"_package.html command has several options for tuning
performance.  Neighbor lists can be built on the GPU or CPU.  Force
calculations can be dynamically balanced across the CPU cores and
GPUs.  GPU-specific settings can be made which can be optimized
for different hardware.  See the "packakge"_package.html command
doc page for details. :l

As described by the "package gpu"_package.html command, GPU
accelerated pair styles can perform computations asynchronously with
CPU computations. The "Pair" time reported by LAMMPS will be the
maximum of the time required to complete the CPU pair style
computations and the time required to complete the GPU pair style
computations. Any time spent for GPU-enabled pair styles for
computations that run simultaneously with "bond"_bond_style.html,
"angle"_angle_style.html, "dihedral"_dihedral_style.html,
"improper"_improper_style.html, and "long-range"_kspace_style.html
calculations will not be included in the "Pair" time. :l

When the {mode} setting for the package gpu command is force/neigh,
the time for neighbor list calculations on the GPU will be added into
the "Pair" time, not the "Neigh" time.  An additional breakdown of the
times required for various tasks on the GPU (data copy, neighbor
calculations, force computations, etc) are output only with the LAMMPS
screen output (not in the log file) at the end of each run.  These
timings represent total time spent on the GPU for each routine,
regardless of asynchronous CPU calculations. :l

The output section "GPU Time Info (average)" reports "Max Mem / Proc".
This is the maximum memory used at one time on the GPU for data
storage by a single MPI process. :l,ule

[Restrictions:]

None.

:line

5.7 USER-CUDA package :h4,link(acc_7)

The USER-CUDA package was developed by Christian Trott (Sandia) while
at U Technology Ilmenau in Germany.  It provides NVIDIA GPU versions
of many pair styles, many fixes, a few computes, and for long-range
Coulombics via the PPPM command.  It has the following general
features:

The package is designed to allow an entire LAMMPS calculation, for
many timesteps, to run entirely on the GPU (except for inter-processor
MPI communication), so that atom-based data (e.g. coordinates, forces)
do not have to move back-and-forth between the CPU and GPU. :ulb,l

The speed-up advantage of this approach is typically better when the
number of atoms per GPU is large :l

Data will stay on the GPU until a timestep where a non-USER-CUDA fix
or compute is invoked.  Whenever a non-GPU operation occurs (fix,
compute, output), data automatically moves back to the CPU as needed.
This may incur a performance penalty, but should otherwise work
transparently. :l

Neighbor lists are constructed on the GPU. :l

The package only supports use of a single MPI task, running on a
single CPU (core), assigned to each GPU. :l,ule

[Required hardware/software:]

To use this package, you need to have an NVIDIA GPU and
install the NVIDIA Cuda software on your system:

Your NVIDIA GPU needs to support Compute Capability 1.3. This list may
help you to find out the Compute Capability of your card:

http://en.wikipedia.org/wiki/Comparison_of_Nvidia_graphics_processing_units

Install the Nvidia Cuda Toolkit (version 3.2 or higher) and the
corresponding GPU drivers.  The Nvidia Cuda SDK is not required, but
we recommend it also be installed.  You can then make sure its sample
projects can be compiled without problems.

[Building LAMMPS with the USER-CUDA package:]

This requires two steps (a,b): build the USER-CUDA library, then build
LAMMPS.

(a) Build the USER-CUDA library

The USER-CUDA library is in lammps/lib/cuda.  If your {CUDA} toolkit
is not installed in the default system directoy {/usr/local/cuda} edit
the file {lib/cuda/Makefile.common} accordingly.

To set options for the library build, type "make OPTIONS", where
{OPTIONS} are one or more of the following. The settings will be
written to the {lib/cuda/Makefile.defaults} and used when
the library is built.

{precision=N} to set the precision level
  N = 1 for single precision (default)
  N = 2 for double precision
  N = 3 for positions in double precision
  N = 4 for positions and velocities in double precision
{arch=M} to set GPU compute capability
  M = 35 for Kepler GPUs
  M = 20 for CC2.0 (GF100/110, e.g. C2050,GTX580,GTX470) (default)
  M = 21 for CC2.1 (GF104/114,  e.g. GTX560, GTX460, GTX450)
  M = 13 for CC1.3 (GF200, e.g. C1060, GTX285)
{prec_timer=0/1} to use hi-precision timers
  0 = do not use them (default)
  1 = use them
  this is usually only useful for Mac machines 
{dbg=0/1} to activate debug mode
  0 = no debug mode (default)
  1 = yes debug mode
  this is only useful for developers
{cufft=1} for use of the CUDA FFT library
  0 = no CUFFT support (default)
  in the future other CUDA-enabled FFT libraries might be supported :pre

To build the library, simply type:

make :pre

If successful, it will produce the files libcuda.a and Makefile.lammps.

Note that if you change any of the options (like precision), you need
to re-build the entire library.  Do a "make clean" first, followed by
"make".

(b) Build LAMMPS

cd lammps/src
make yes-user-cuda
make machine :pre

Note that if you change the USER-CUDA library precision (discussed
above), you also need to re-install the USER-CUDA package and re-build
LAMMPS, so that all affected files are re-compiled and linked to the
new USER-CUDA library.

[Running with the USER-CUDA package:]

The bench/CUDA directories has scripts that can be run with the
USER-CUDA package, as well as detailed instructions on how to run
them.

To run with the USER-CUDA package, there are 3 basic issues (a,b,c) to
address:

(a) Use one MPI task per GPU

This is a requirement of the USER-CUDA package, i.e. you cannot
use multiple MPI tasks per physical GPU.  So if you are running
on nodes with 1 or 2 GPUs, use the mpirun or mpiexec command
to specify 1 or 2 MPI tasks per node.

If the nodes have more than 1 GPU, you must use the "package
cuda"_package.html command near the top of your input script to
specify that more than 1 GPU will be used (the default = 1).

(b) Enable the USER-CUDA package

The "-c on" or "-cuda on" "command-line
switch"_Section_start.html#start_7 must be used when launching LAMMPS.

(c) Use USER-CUDA-accelerated styles

This can be done by explicitly adding a "cuda" suffix to any supported
style in your input script:

pair_style lj/cut/cuda 2.5 :pre

Or you can run with the "-sf cuda" "command-line
switch"_Section_start.html#start_7, which will automatically append
"cuda" to styles that support it.

lmp_machine -sf cuda -in in.script
mpirun -np 4 lmp_machine -sf cuda -in in.script :pre

Using the "suffix cuda" command in your input script does the same
thing.

[Speed-ups to expect:]

The performance of a GPU versus a multi-core CPU is a function of your
hardware, which pair style is used, the number of atoms/GPU, and the
precision used on the GPU (double, single, mixed).

See the "Benchmark page"_http://lammps.sandia.gov/bench.html of the
LAMMPS web site for performance of the USER-CUDA package on different
hardware.

[Guidelines for best performance:]

The USER-CUDA package offers more speed-up relative to CPU performance
when the number of atoms per GPU is large, e.g. on the order of tens
or hundreds of 1000s. :ulb,l

As noted above, this package will continue to run a simulation
entirely on the GPU(s) (except for inter-processor MPI communication),
for multiple timesteps, until a CPU calculation is required, either by
a fix or compute that is non-GPU-ized, or until output is performed
(thermo or dump snapshot or restart file).  The less often this
occurs, the faster your simulation will run. :l,ule

[Restrictions:]

None.

:line

5.8 KOKKOS package :h4,link(acc_8)

The KOKKOS package was developed primaritly by Christian Trott
(Sandia) with contributions of various styles by others, including
Sikandar Mashayak (UIUC).  The underlying Kokkos library was written
primarily by Carter Edwards, Christian Trott, and Dan Sunderland (all
Sandia).

The KOKKOS package contains versions of pair, fix, and atom styles
that use data structures and macros provided by the Kokkos library,
which is included with LAMMPS in lib/kokkos.

The Kokkos library is part of
"Trilinos"_http://trilinos.sandia.gov/packages/kokkos and is a
templated C++ library that provides two key abstractions for an
application like LAMMPS.  First, it allows a single implementation of
an application kernel (e.g. a pair style) to run efficiently on
different kinds of hardware, such as a GPU, Intel Phi, or many-core
chip.

The Kokkos library also provides data abstractions to adjust (at
compile time) the memory layout of basic data structures like 2d and
3d arrays and allow the transparent utilization of special hardware
load and store operations.  Such data structures are used in LAMMPS to
store atom coordinates or forces or neighbor lists.  The layout is
chosen to optimize performance on different platforms.  Again this
functionality is hidden from the developer, and does not affect how
the kernel is coded.

These abstractions are set at build time, when LAMMPS is compiled with
the KOKKOS package installed.  This is done by selecting a "host" and
"device" to build for, compatible with the compute nodes in your
machine (one on a desktop machine or 1000s on a supercomputer).

All Kokkos operations occur within the context of an individual MPI
task running on a single node of the machine.  The total number of MPI
tasks used by LAMMPS (one or multiple per compute node) is set in the
usual manner via the mpirun or mpiexec commands, and is independent of
Kokkos.

Kokkos provides support for two different modes of execution per MPI
task.  This means that computational tasks (pairwise interactions,
neighbor list builds, time integration, etc) can be parallelized for
one or the other of the two modes.  The first mode is called the
"host" and is one or more threads running on one or more physical CPUs
(within the node).  Currently, both multi-core CPUs and an Intel Phi
processor (running in native mode) are supported.  The second mode is
called the "device" and is an accelerator chip of some kind.
Currently only an NVIDIA GPU is supported.  If your compute node does
not have a GPU, then there is only one mode of execution, i.e. the
host and device are the same.

[Required hardware/software:]

The KOKKOS package can be used to build and run
LAMMPS on the following kinds of hardware configurations:

CPU-only: one MPI task per CPU core (MPI-only, but using KOKKOS styles)
CPU-only: one or a few MPI tasks per node with additional threading via OpenMP
Phi: on one or more Intel Phi coprocessors (per node)
GPU: on the GPUs of a node with additional OpenMP threading on the CPUs :ul

Intel Xeon Phi coprocessors are supported in "native" mode only.

Only NVIDIA GPUs are currently supported.

IMPORTANT NOTE: For good performance of the KOKKOS package on GPUs,
you must have Kepler generation GPUs (or later).  The Kokkos library
exploits texture cache options not supported by Telsa generation GPUs
(or older).

To build the KOKKOS package for GPUs, NVIDIA Cuda software must be
installed on your system.  See the discussion above for the USER-CUDA
and GPU packages for details of how to check and do this.

[Building LAMMPS with the KOKKOS package:]

Unlike other acceleration packages discussed in this section, the
Kokkos library in lib/kokkos does not have to be pre-built before
building LAMMPS itself.  Instead, options for the Kokkos library are
specified at compile time, when LAMMPS itself is built.  This can be
done in one of two ways, as discussed below.

Here are examples of how to build LAMMPS for the different compute-node
configurations listed above.

CPU-only (run all-MPI or with OpenMP threading):

cd lammps/src
make yes-kokkos
make g++ OMP=yes :pre

Intel Xeon Phi:

cd lammps/src
make yes-kokkos
make g++ OMP=yes MIC=yes :pre

CPUs and GPUs:

cd lammps/src
make yes-kokkos
make cuda CUDA=yes :pre

These examples set the KOKKOS-specific OMP, MIC, CUDA variables on the
make command line which requires a GNU-compatible make command.  Try
"gmake" if your system's standard make complains.  

IMPORTANT NOTE: If you build using make line variables and re-build
LAMMPS twice with different KOKKOS options and the *same* target,
e.g. g++ in the first two examples above, then you *must* perform a
"make clean-all" or "make clean-machine" before each build.  This is
to force all the KOKKOS-dependent files to be re-compiled with the new
options.

You can also hardwire these variables in the specified machine
makefile, e.g. src/MAKE/Makefile.g++ in the first two examples above,
with a line like:

MIC = yes :pre

Note that if you build LAMMPS multiple times in this manner, using
different KOKKOS options (defined in different machine makefiles), you
do not have to worry about doing a "clean" in between.  This is
because the targets will be different.

IMPORTANT NOTE: The 3rd example above for a GPU, uses a different
machine makefile, in this case src/MAKE/Makefile.cuda, which is
included in the LAMMPS distribution.  To build the KOKKOS package for
a GPU, this makefile must use the NVIDA "nvcc" compiler.  And it must
have a CCFLAGS -arch setting that is appropriate for your NVIDIA
hardware and installed software.  Typical values for -arch are given
in "Section 2.3.4"_Section_start.html#start_3_4 of the manual, as well
as other settings that must be included in the machine makefile, if
you create your own.

There are other allowed options when building with the KOKKOS package.
As above, They can be set either as variables on the make command line
or in the machine makefile in the src/MAKE directory.  See "Section
2.3.4"_Section_start.html#start_3_4 of the manual for details.

IMPORTANT NOTE: Currently, there are no precision options with the
KOKKOS package.  All compilation and computation is performed in
double precision.

[Running with the KOKKOS package:]

The examples/kokkos and bench/KOKKOS directories have scripts that can
be run with the KOKKOS package, as well as detailed instructions on
how to run them.

There are 3 issues (a,b,c) to address:

(a) Launching LAMMPS in different KOKKOS modes

Here are examples of how to run LAMMPS for the different compute-node
configurations listed above.

Note that the -np setting for the mpirun command in these examples is
for runs on a single node.  To scale these examples up to run on a
system with N compute nodes, simply multiply the -np setting by N.

CPU-only, dual hex-core CPUs:

mpirun -np 12 lmp_g++ -in in.lj      # MPI-only mode with no Kokkos
mpirun -np 12 lmp_g++ -k on -sf kk -in in.lj      # MPI-only mode with Kokkos
mpirun -np 1 lmp_g++ -k on t 12 -sf kk -in in.lj     # one MPI task, 12 threads
mpirun -np 2 lmp_g++ -k on t 6 -sf kk -in in.lj      # two MPI tasks, 6 threads/task :pre

Intel Phi with 61 cores (240 total usable cores, with 4x hardware threading):

mpirun -np 12 lmp_g++ -k on t 20 -sf kk -in in.lj      # 12*20 = 240
mpirun -np 15 lmp_g++ -k on t 16 -sf kk -in in.lj
mpirun -np 30 lmp_g++ -k on t 8 -sf kk -in in.lj
mpirun -np 1 lmp_g++ -k on t 240 -sf kk -in in.lj :pre

Dual hex-core CPUs and a single GPU:

mpirun -np 1 lmp_cuda -k on t 6 -sf kk -in in.lj       # one MPI task, 6 threads on CPU :pre

Dual 8-core CPUs and 2 GPUs:

mpirun -np 2 lmp_cuda -k on t 8 g 2 -sf kk -in in.lj   # two MPI tasks, 8 threads per CPU :pre

(b) Enable the KOKKOS package

As illustrated above, the "-k on" or "-kokkos on" "command-line
switch"_Section_start.html#start_7 must be used when launching LAMMPS.

As documented "here"_Section_start.html#start_7, the command-line
swithc allows for several options.  Commonly used ones, as illustrated
above, are:

-k on t Nt : specifies how many threads per MPI task to use within a
compute node.  For good performance, the product of MPI tasks *
threads/task should not exceed the number of physical cores on a CPU
or Intel Phi (including hardware threading, e.g. 240). :ulb,l

-k on g Ng : specifies how many GPUs per compute node are available.
The default is 1, so this should be specified is you have 2 or more
GPUs per compute node. :l,ule

(c) Use KOKKOS-accelerated styles

This can be done by explicitly adding a "kk" suffix to any supported
style in your input script:

pair_style lj/cut/kk 2.5 :pre

Or you can run with the "-sf kk" "command-line
switch"_Section_start.html#start_7, which will automatically append
"kk" to styles that support it.

lmp_machine -sf kk -in in.script
mpirun -np 4 lmp_machine -sf kk -in in.script :pre

Using the "suffix kk" command in your input script does the same
thing.

[Speed-ups to expect:]

The performance of KOKKOS running in different modes is a function of
your hardware, which KOKKOS-enable styles are used, and the problem
size.

Generally speaking, the following rules of thumb apply:

When running on CPUs only, with a single thread per MPI task,
performance of a KOKKOS style is somewhere between the standard
(un-accelerated) styles (MPI-only mode), and those provided by the
USER-OMP package.  However the difference between all 3 is small (less
than 20%). :ulb,l

When running on CPUs only, with multiple threads per MPI task,
performance of a KOKKOS style is a bit slower than the USER-OMP
package. :l

When running on GPUs, KOKKOS currently out-performs the 
USER-CUDA and GPU packages. :l

When running on Intel Xeon Phi, KOKKOS is not as fast as
the USER-INTEL package, which is optimized for that hardware. :l,ule

See the "Benchmark page"_http://lammps.sandia.gov/bench.html of the
LAMMPS web site for performance of the KOKKOS package on different
hardware.

[Guidelines for best performance:]

Here are guidline for using the KOKKOS package on the different hardware
configurations listed above.

Many of the guidelines use the "package kokkos"_package.html command
See its doc page for details and default settings.  Experimenting with
its options can provide a speed-up for specific calculations.

[Running on a multi-core CPU:]

If N is the number of physical cores/node, then the number of MPI
tasks/node * number of threads/task should not exceed N, and should
typically equal N.  Note that the default threads/task is 1, as set by
the "t" keyword of the -k "command-line
switch"_Section_start.html#start_7.  If you do not change this, no
additional parallelism (beyond MPI) will be invoked on the host
CPU(s).

You can compare the performance running in different modes:
  
run with 1 MPI task/node and N threads/task
run with N MPI tasks/node and 1 thread/task
run with settings in between these extremes :ul

Examples of mpirun commands in these modes, for nodes with dual
hex-core CPUs and no GPU, are shown above.

When using KOKKOS to perform multi-threading, it is important for
performance to bind both MPI tasks to physical cores, and threads to
physical cores, so they do not migrate during a simulation.

If you are not certain MPI tasks are being bound (check the defaults
for your MPI installation), it can be forced with these flags:

OpenMPI 1.8: mpirun -np 2 -bind-to socket -map-by socket ./lmp_openmpi ...
Mvapich2 2.0: mpiexec -np 2 -bind-to socket -map-by socket ./lmp_mvapich ... :pre

For binding threads with the KOKKOS OMP option, use thread affinity
environment variables to force binding.  With OpenMP 3.1 (gcc 4.7 or
later, intel 12 or later) setting the environment variable
OMP_PROC_BIND=true should be sufficient.  For binding threads with the
KOKKOS pthreads option, compile LAMMPS the KOKKOS HWLOC=yes option, as
discussed in "Section 2.3.4"_Sections_start.html#start_3_4 of the
manual.

[Running on GPUs:]

Insure the -arch setting in the machine makefile you are using,
e.g. src/MAKE/Makefile.cuda, is correct for your GPU hardware/software
(see "this section"_Section_start.html#start_3_4 of the manual for
details).

The -np setting of the mpirun command should set the number of MPI
tasks/node to be equal to the # of physical GPUs on the node. 

Use the "-kokkos command-line switch"_Section_commands.html#start_7 to
specify the number of GPUs per node, and the number of threads per MPI
task.  As above for multi-core CPUs (and no GPU), if N is the number
of physical cores/node, then the number of MPI tasks/node * number of
threads/task should not exceed N.  With one GPU (and one MPI task) it
may be faster to use less than all the available cores, by setting
threads/task to a smaller value.  This is because using all the cores
on a dual-socket node will incur extra cost to copy memory from the
2nd socket to the GPU.

Examples of mpirun commands that follow these rules, for nodes with
dual hex-core CPUs and one or two GPUs, are shown above.

When using a GPU, you will achieve the best performance if your input
script does not use any fix or compute styles which are not yet
Kokkos-enabled.  This allows data to stay on the GPU for multiple
timesteps, without being copied back to the host CPU.  Invoking a
non-Kokkos fix or compute, or performing I/O for
"thermo"_thermo_style.html or "dump"_dump.html output will cause data
to be copied back to the CPU.

You cannot yet assign multiple MPI tasks to the same GPU with the
KOKKOS package.  We plan to support this in the future, similar to the
GPU package in LAMMPS.

You cannot yet use both the host (multi-threaded) and device (GPU)
together to compute pairwise interactions with the KOKKOS package.  We
hope to support this in the future, similar to the GPU package in
LAMMPS.

[Running on an Intel Phi:]

Kokkos only uses Intel Phi processors in their "native" mode, i.e.
not hosted by a CPU.

As illustrated above, build LAMMPS with OMP=yes (the default) and
MIC=yes.  The latter insures code is correctly compiled for the Intel
Phi.  The OMP setting means OpenMP will be used for parallelization on
the Phi, which is currently the best option within Kokkos.  In the
future, other options may be added.

Current-generation Intel Phi chips have either 61 or 57 cores.  One
core should be excluded for running the OS, leaving 60 or 56 cores.
Each core is hyperthreaded, so there are effectively N = 240 (4*60) or
N = 224 (4*56) cores to run on.

The -np setting of the mpirun command sets the number of MPI
tasks/node.  The "-k on t Nt" command-line switch sets the number of
threads/task as Nt.  The product of these 2 values should be N, i.e.
240 or 224.  Also, the number of threads/task should be a multiple of
4 so that logical threads from more than one MPI task do not run on
the same physical core.

Examples of mpirun commands that follow these rules, for Intel Phi
nodes with 61 cores, are shown above.

[Restrictions:]

As noted above, if using GPUs, the number of MPI tasks per compute
node should equal to the number of GPUs per compute node.  In the
future Kokkos will support assigning multiple MPI tasks to a single
GPU.

Currently Kokkos does not support AMD GPUs due to limits in the
available backend programming models.  Specifically, Kokkos requires
extensive C++ support from the Kernel language.  This is expected to
change in the future.

:line

5.9 USER-INTEL package :h4,link(acc_9)

The USER-INTEL package was developed by Mike Brown at Intel
Corporation.  It provides a capability to accelerate simulations by
offloading neighbor list and non-bonded force calculations to Intel(R)
Xeon Phi(TM) coprocessors.  Additionally, it supports running
simulations in single, mixed, or double precision with vectorization,
even if a coprocessor is not present, i.e. on an Intel(R) CPU.  The
same C++ code is used for both cases.  When offloading to a
coprocessor, the routine is run twice, once with an offload flag.

The USER-INTEL package can be used in tandem with the USER-OMP
package.  This is useful when offloading pair style computations to
coprocessors, so that other styles not supported by the USER-INTEL
package, e.g. bond, angle, dihedral, improper, and long-range
electrostatics, can be run simultaneously in threaded mode on CPU
cores.  Since less MPI tasks than CPU cores will typically be invoked
when running with coprocessors, this enables the extra cores to be
utilized for useful computation.

If LAMMPS is built with both the USER-INTEL and USER-OMP packages
intsalled, this mode of operation is made easier to use, because the
"-suffix intel" "command-line switch"_Section_start.html#start_7 or
the "suffix intel"_suffix.html command will both set a second-choice
suffix to "omp" so that styles from the USER-OMP package will be used
if available, after first testing if a style from the USER-INTEL
package is available.

[Required hardware/software:]

To use the offload option, you must have one or more Intel(R) Xeon
Phi(TM) coprocessors.

Optimizations for vectorization have only been tested with the
Intel(R) compiler.  Use of other compilers may not result in
vectorization or give poor performance.

Use of an Intel C++ compiler is reccommended, but not required.  The
compiler must support the OpenMP interface.

[Building LAMMPS with the USER-INTEL package:]

Include the package and build LAMMPS.  

cd lammps/src
make yes-user-intel
make yes-user-omp (if desired)
make machine :pre

If the USER-OMP package is also installed, you can use styles from
both packages, as described below.

The lo-level src/MAKE/Makefile.machine needs a flag for OpenMP support
in both the CCFLAGS and LINKFLAGS variables, which is {-openmp} for
Intel compilers.  You also need to add -DLAMMPS_MEMALIGN=64 and
-restrict to CCFLAGS.

If you are compiling on the same architecture that will be used for
the runs, adding the flag {-xHost} to CCFLAGS will enable
vectorization with the Intel(R) compiler.

In order to build with support for an Intel(R) coprocessor, the flag
{-offload} should be added to the LINKFLAGS line and the flag
-DLMP_INTEL_OFFLOAD should be added to the CCFLAGS line.

Note that the machine makefiles Makefile.intel and
Makefile.intel_offload are included in the src/MAKE directory with
options that perform well with the Intel(R) compiler. The latter file
has support for offload to coprocessors; the former does not.

If using an Intel compiler, it is recommended that Intel(R) Compiler
2013 SP1 update 1 be used.  Newer versions have some performance
issues that are being addressed. If using Intel(R) MPI, version 5 or
higher is recommended.

[Running with the USER-INTEL package:]

The examples/intel directory has scripts that can be run with the
USER-INTEL package, as well as detailed instructions on how to run
them.

Note that the total number of MPI tasks used by LAMMPS (one or
multiple per compute node) is set in the usual manner via the mpirun
or mpiexec commands, and is independent of the USER-INTEL package.

To run with the USER-INTEL package, there are 3 basic issues (a,b,c)
to address:

(a) Specify how many threads per MPI task to use on the CPU.

Whether using the USER-INTEL package to offload computations to
Intel(R) Xeon Phi(TM) coprocessors or not, work performed on the CPU
can be multi-threaded via the USER-OMP package, assuming the USER-OMP
package was also installed when LAMMPS was built.

In this case, the instructions above for the USER-OMP package, in its
"Running with the USER-OMP package" sub-section apply here as well.

You can specify the number of threads per MPI task via the
OMP_NUM_THREADS environment variable or the "package omp"_package.html
command.  The product of MPI tasks * threads/task should not exceed
the physical number of cores on the CPU (per node), otherwise
performance will suffer.

Note that the threads per MPI task setting is completely independent
of the number of threads used on the coprocessor.  Only the "package
intel"_package.html command can be used to control thread counts on
the coprocessor.

(b) Enable the USER-INTEL package

This can be done in one of two ways.  Use a "package intel"_package.html
command near the top of your input script.

Or use the "-sf intel" "command-line
switch"_Section_start.html#start_7, which will automatically invoke
the command "package intel * mixed balance -1 offload_cards 1
offload_tpc 4 offload_threads 240".  Note that this specifies mixed
precision and use of a single Xeon Phi(TM) coprocessor (per node), so
you must specify the package command in your input script explicitly
if you want a different precision or to use multiple Phi coprocessor
per node.  Also note that the balance and offload keywords are ignored
if you did not build LAMMPS with offload support for a coprocessor, as
descibed above.

(c) Use USER-INTEL-accelerated styles

This can be done by explicitly adding an "intel" suffix to any
supported style in your input script:

pair_style lj/cut/intel 2.5 :pre

Or you can run with the "-sf intel" "command-line
switch"_Section_start.html#start_7, which will automatically append
"intel" to styles that support it.

lmp_machine -sf intel -in in.script
mpirun -np 4 lmp_machine -sf intel -in in.script :pre

Using the "suffix intel" command in your input script does the same
thing.

IMPORTANT NOTE: Using an "intel" suffix in any of the above modes,
actually invokes two suffixes, "intel" and "omp".  "Intel" is tried
first, and if the style does not support it, "omp" is tried next.  If
neither is supported, the default non-suffix style is used.

[Speed-ups to expect:]

If LAMMPS was not built with coprocessor support when including the
USER-INTEL package, then acclerated styles will run on the CPU using
vectorization optimizations and the specified precision.  This may
give a substantial speed-up for a pair style, particularly if mixed or
single precision is used.

If LAMMPS was built with coproccesor support, the pair styles will run
on one or more Intel(R) Xeon Phi(TM) coprocessors (per node).  The
performance of a Xeon Phi versus a multi-core CPU is a function of
your hardware, which pair style is used, the number of
atoms/coprocessor, and the precision used on the coprocessor (double,
single, mixed).

See the "Benchmark page"_http://lammps.sandia.gov/bench.html of the
LAMMPS web site for performance of the USER-INTEL package on different
hardware.

[Guidelines for best performance on an Intel(R) Xeon Phi(TM)
coprocessor:]

The default for the "package intel"_package.html command is to have
all the MPI tasks on a given compute node use a single Xeon Phi(TM)
coprocessor.  In general, running with a large number of MPI tasks on
each node will perform best with offload.  Each MPI task will
automatically get affinity to a subset of the hardware threads
available on the coprocessor.  For example, if your card has 61 cores,
with 60 cores available for offload and 4 hardware threads per core
(240 total threads), running with 24 MPI tasks per node will cause
each MPI task to use a subset of 10 threads on the coprocessor.  Fine
tuning of the number of threads to use per MPI task or the number of
threads to use per core can be accomplished with keyword settings of
the "package intel"_package.html command. :ulb,l

If desired, only a fraction of the pair style computation can be
offloaded to the coprocessors.  This is accomplished by setting a
balance fraction in the "package intel"_package.html command.  A
balance of 0 runs all calculations on the CPU.  A balance of 1 runs
all calculations on the coprocessor.  A balance of 0.5 runs half of
the calculations on the coprocessor.  Setting the balance to -1 (the
default) will enable dynamic load balancing that continously adjusts
the fraction of offloaded work throughout the simulation.  This option
typically produces results within 5 to 10 percent of the optimal fixed
balance. :l

When using offload with CPU hyperthreading disabled, it may help
performance to use fewer MPI tasks and OpenMP threads than available
cores.  This is due to the fact that additional threads are generated
internally to handle the asynchronous offload tasks. :l

If you have multiple coprocessors on each compute node, the
{offload_cards} keyword can be specified with the "package
intel"_package.html command. :l

If running short benchmark runs with dynamic load balancing, adding a
short warm-up run (10-20 steps) will allow the load-balancer to find a
near-optimal setting that will carry over to additional runs. :l

If pair computations are being offloaded to an Intel(R) Xeon Phi(TM)
coprocessor, a diagnostic line is printed to the screen (not to the
log file), during the setup phase of a run, indicating that offload
mode is being used and indicating the number of coprocessor threads
per MPI task.  Additionally, an offload timing summary is printed at
the end of each run.  When offloading, the frequency for "atom
sorting"_atom_modify.html is changed to 1 so that the per-atom data is
effectively sorted at every rebuild of the neighbor lists. :l

For simulations with long-range electrostatics or bond, angle,
dihedral, improper calculations, computation and data transfer to the
coprocessor will run concurrently with computations and MPI
communications for these calculations on the host CPU.  The USER-INTEL
package has two modes for deciding which atoms will be handled by the
coprocessor.  This choice is controlled with the "offload_ghost"
keyword of the "package intel"_package.html command.  When set to 0,
ghost atoms (atoms at the borders between MPI tasks) are not offloaded
to the card.  This allows for overlap of MPI communication of forces
with computation on the coprocessor when the "newton"_newton.html
setting is "on".  The default is dependent on the style being used,
however, better performance may be achieved by setting this option
explictly. :l,ule

[Restrictions:]

When offloading to a coprocessor, "hybrid"_pair_hybrid.html styles
that require skip lists for neighbor builds cannot be offloaded.
Using "hybrid/overlay"_pair_hybrid.html is allowed.  Only one intel
accelerated style may be used with hybrid styles.
"Special_bonds"_special_bonds.html exclusion lists are not currently
supported with offload, however, the same effect can often be
accomplished by setting cutoffs for excluded atom types to 0.  None of
the pair styles in the USER-INTEL package currently support the
"inner", "middle", "outer" options for rRESPA integration via the
"run_style respa"_run_style.html command; only the "pair" option is
supported.

:line

5.10 Comparison of GPU and USER-CUDA and KOKKOS packages :h4,link(acc_10)

All 3 of these packages accelerate a LAMMPS calculation using NVIDIA
hardware, but they do it in different ways.

NOTE: this section still needs to be re-worked with additional KOKKOS
information.

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.

[Guidelines for using each package optimally:]

The GPU package allows you to assign multiple CPUs (cores) to a single
GPU (a common configuration for "hybrid" 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. :ulb,l

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. :l

The GPU package is often faster than the USER-CUDA package, if the
number of atoms per GPU is "small".  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. :l

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. :l

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. :l,ule

[Differences between the two packages:]

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. :ulb,l

The USER-CUDA package does not support acceleration for minimization. :l

The USER-CUDA package does not support hybrid pair styles. :l

The USER-CUDA package can order atoms in the neighbor list differently
from run to run resulting in a different order for force accumulation. :l

The USER-CUDA package has a limit on the number of atom types that can be
used in a simulation. :l

The GPU package requires neighbor lists to be built on the CPU when using
exclusion lists or a triclinic simulation box. :l

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. :l,ule

[Examples:]

The LAMMPS distribution has two directories with sample input scripts
for the GPU and USER-CUDA packages.

lammps/examples/gpu = GPU package files
lammps/examples/USER/cuda = USER-CUDA package files :ul

These contain input scripts for identical systems, so they can be used
to benchmark the performance of both packages on your system.