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651 lines
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HTML
<HTML>
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<CENTER><A HREF = "Section_packages.html">Previous Section</A> - <A HREF = "http://lammps.sandia.gov">LAMMPS WWW Site</A> -
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<A HREF = "Manual.html">LAMMPS Documentation</A> - <A HREF = "Section_commands.html#comm">LAMMPS Commands</A> - <A HREF = "Section_howto.html">Next
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Section</A>
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</CENTER>
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<HR>
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<H3>5. Using accelerated CPU and GPU styles
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</H3>
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<P>Accelerated versions of various <A HREF = "pair_style.html">pair_style</A>,
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<A HREF = "fix.html">fixes</A>, <A HREF = "compute.html">computes</A>, and other commands have
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been added to LAMMPS, which will typically run faster than the
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standard non-accelerated versions, if you have the appropriate
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hardware on your system.
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</P>
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<P>The accelerated styles have the same name as the standard styles,
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except that a suffix is appended. Otherwise, the syntax for the
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command is identical, their functionality is the same, and the
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numerical results it produces should also be identical, except for
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precision and round-off issues.
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</P>
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<P>For example, all of these variants of the basic Lennard-Jones pair
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style exist in LAMMPS:
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</P>
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<UL><LI><A HREF = "pair_lj.html">pair_style lj/cut</A>
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<LI><A HREF = "pair_lj.html">pair_style lj/cut/opt</A>
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<LI><A HREF = "pair_lj.html">pair_style lj/cut/omp</A>
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<LI><A HREF = "pair_lj.html">pair_style lj/cut/gpu</A>
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<LI><A HREF = "pair_lj.html">pair_style lj/cut/cuda</A>
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</UL>
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<P>Assuming you have built LAMMPS with the appropriate package, these
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styles can be invoked by specifying them explicitly in your input
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script. Or you can use the <A HREF = "Section_start.html#start_6">-suffix command-line
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switch</A> to invoke the accelerated versions
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automatically, without changing your input script. The
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<A HREF = "suffix.html">suffix</A> command allows you to set a suffix explicitly and
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to turn off/on the comand-line switch setting, both from within your
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input script.
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</P>
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<P>Styles with an "opt" suffix are part of the OPT package and typically
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speed-up the pairwise calculations of your simulation by 5-25%.
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</P>
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<P>Styles with an "omp" suffix are part of the USER-OMP package and allow
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a pair-style to be run in multi-threaded mode using OpenMP. This can
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be useful on nodes with high-core counts when using less MPI processes
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than cores is advantageous, e.g. when running with PPPM so that FFTs
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are run on fewer MPI processors or when the many MPI tasks would
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overload the available bandwidth for communication.
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</P>
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<P>Styles with a "gpu" or "cuda" suffix are part of the GPU or USER-CUDA
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packages, and can be run on NVIDIA GPUs associated with your CPUs.
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The speed-up due to GPU usage depends on a variety of factors, as
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discussed below.
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</P>
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<P>To see what styles are currently available in each of the accelerated
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packages, see <A HREF = "Section_commands.html#cmd_5">this section</A> of the
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manual. A list of accelerated styles is included in the pair, fix,
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compute, and kspace sections.
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</P>
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<P>The following sections explain:
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</P>
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<UL><LI>what hardware and software the accelerated styles require
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<LI>how to build LAMMPS with the accelerated packages in place
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<LI>what changes (if any) are needed in your input scripts
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<LI>guidelines for best performance
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<LI>speed-ups you can expect
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</UL>
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<P>The final section compares and contrasts the GPU and USER-CUDA
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packages, since they are both designed to use NVIDIA GPU hardware.
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</P>
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5.1 <A HREF = "#acc_1">OPT package</A><BR>
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5.2 <A HREF = "#acc_2">USER-OMP package</A><BR>
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5.3 <A HREF = "#acc_3">GPU package</A><BR>
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5.4 <A HREF = "#acc_4">USER-CUDA package</A><BR>
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5.5 <A HREF = "#acc_5">Comparison of GPU and USER-CUDA packages</A> <BR>
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<HR>
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<HR>
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<H4><A NAME = "acc_1"></A>5.1 OPT package
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</H4>
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<P>The OPT package was developed by James Fischer (High Performance
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Technologies), David Richie, and Vincent Natoli (Stone Ridge
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Technologies). It contains a handful of pair styles whose compute()
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methods were rewritten in C++ templated form to reduce the overhead
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due to if tests and other conditional code.
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</P>
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<P>The procedure for building LAMMPS with the OPT package is simple. It
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is the same as for any other package which has no additional library
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dependencies:
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</P>
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<PRE>make yes-opt
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make machine
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</PRE>
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<P>If your input script uses one of the OPT pair styles,
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you can run it as follows:
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</P>
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<PRE>lmp_machine -sf opt < in.script
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mpirun -np 4 lmp_machine -sf opt < in.script
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</PRE>
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<P>You should see a reduction in the "Pair time" printed out at the end
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of the run. On most machines and problems, this will typically be a 5
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to 20% savings.
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</P>
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<HR>
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<HR>
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<H4><A NAME = "acc_2"></A>5.2 USER-OMP package
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</H4>
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<P>The USER-OMP package was developed by Axel Kohlmeyer at Temple University.
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It provides multi-threaded versions of most pair styles, all dihedral
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styles and a few fixes in LAMMPS. The package currently uses the OpenMP
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interface which requires using a specific compiler flag in the makefile
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to enable multiple threads; without this flag the corresponding pair
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styles will still be compiled and work, but do not support multi-threading.
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</P>
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<P><B>Building LAMMPS with the USER-OMP package:</B>
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</P>
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<P>The procedure for building LAMMPS with the USER-OMP package is simple.
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You have to edit your machine specific makefile to add the flag to
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enable OpenMP support to the CCFLAGS and LINKFLAGS variables. For the
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GNU compilers for example this flag is called <I>-fopenmp</I>. Check your
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compiler documentation to find out which flag you need to add.
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The rest of the compilation is the same as for any other package which
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has no additional library dependencies:
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</P>
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<PRE>make yes-user-omp
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make machine
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</PRE>
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<P>Please note that this will only install accelerated versions
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of styles that are already installed, so you want to install
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this package as the last package, or else you may be missing
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some accelerated styles. If you plan to uninstall some package,
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you should first uninstall the USER-OMP package then the other
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package and then re-install USER-OMP, to make sure that there
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are no orphaned <I>omp</I> style files present, which would lead to
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compilation errors.
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</P>
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<P>If your input script uses one of regular styles that are also
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exist as an OpenMP version in the USER-OMP package you can run
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it as follows:
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</P>
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<PRE>env OMP_NUM_THREADS=4 lmp_serial -sf omp -in in.script
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env OMP_NUM_THREADS=2 mpirun -np 2 lmp_machine -sf omp -in in.script
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mpirun -x OMP_NUM_THREADS=2 -np 2 lmp_machine -sf omp -in in.script
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</PRE>
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<P>The value of the environment variable OMP_NUM_THREADS determines how
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many threads per MPI task are launched. All three examples above use
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a total of 4 CPU cores. For different MPI implementations the method
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to pass the OMP_NUM_THREADS environment variable to all processes is
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different. Two different variants, one for MPICH and OpenMPI, respectively
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are shown above. Please check the documentation of your MPI installation
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for additional details. Alternatively, the value provided by OMP_NUM_THREADS
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can be overridded with the <A HREF = "package.html">package omp</A> command.
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Depending on which styles are accelerated in your input, you should
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see a reduction in the "Pair time" and/or "Bond time" and "Loop time"
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printed out at the end of the run. The optimal ratio of MPI to OpenMP
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can vary a lot and should always be confirmed through some benchmark
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runs for the current system and on the current machine.
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</P>
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<P><B>Restrictions:</B>
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</P>
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<P>None of the pair styles in the USER-OMP package support the "inner",
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"middle", "outer" options for r-RESPA integration, only the "pair"
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option is supported.
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</P>
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<P><B>Parallel efficiency and performance tips:</B>
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</P>
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<P>In most simple cases the MPI parallelization in LAMMPS is more
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efficient than multi-threading implemented in the USER-OMP package.
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Also the parallel efficiency varies between individual styles.
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On the other hand, in many cases you still want to use the <I>omp</I> version
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- even when compiling or running without OpenMP support - since they
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all contain optimizations similar to those in the OPT package, which
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can result in serial speedup.
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</P>
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<P>Using multi-threading is most effective under the following circumstances:
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</P>
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<UL><LI>Individual compute nodes have a significant number of CPU cores
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but the CPU itself has limited memory bandwidth, e.g. Intel Xeon 53xx
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(Clovertown) and 54xx (Harpertown) quad core processors. Running
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one MPI task per CPU core will result in significant performance
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degradation, so that running with 4 or even only 2 MPI tasks per
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nodes is faster. Running in hybrid MPI+OpenMP mode will reduce the
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inter-node communication bandwidth contention in the same way,
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but offers and additional speedup from utilizing the otherwise
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idle CPU cores.
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<LI>The interconnect used for MPI communication is not able to provide
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sufficient bandwidth for a large number of MPI tasks per node.
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This applies for example to running over gigabit ethernet or
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on Cray XT4 or XT5 series supercomputers. Same as in the aforementioned
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case this effect worsens with using an increasing number of nodes.
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<LI>The input is a system that has an inhomogeneous particle density
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which cannot be mapped well to the domain decomposition scheme
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that LAMMPS employs. While this can be to some degree alleviated
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through using the <A HREF = "processors.html">processors</A> keyword, multi-threading
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provides a parallelism that parallelizes over the number of particles
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not their distribution in space.
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<LI>Finally, multi-threaded styles can improve performance when running
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LAMMPS in "capability mode", i.e. near the point where the MPI
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parallelism scales out. This can happen in particular when using
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as kspace style for long-range electrostatics. Here the scaling
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of the kspace style is the performance limiting factor and using
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multi-threaded styles allows to operate the kspace style at the
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limit of scaling and then increase performance parallelizing
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the real space calculations with hybrid MPI+OpenMP. Sometimes
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additional speedup can be achived by increasing the real-space
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coulomb cutoff and thus reducing the work in the kspace part.
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</UL>
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<P>The best parallel efficiency from <I>omp</I> styles is typically
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achieved when there is at least one MPI task per physical
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processor, i.e. socket or die.
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</P>
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<P>Using threads on hyper-threading enabled cores is usually
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counterproductive, as the cost in additional memory bandwidth
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requirements is not offset by the gain in CPU utilization
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through hyper-threading.
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</P>
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<P>A description of the multi-threading strategy and some performance
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examples are <A HREF = "http://sites.google.com/site/akohlmey/software/lammps-icms/lammps-icms-tms2011-talk.pdf?attredirects=0&d=1">presented here</A>
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</P>
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<HR>
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<HR>
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<H4><A NAME = "acc_3"></A>5.3 GPU package
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</H4>
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<P>The GPU package was developed by Mike Brown at ORNL. It provides GPU
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versions of several pair styles and for long-range Coulombics via the
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PPPM command. It has the following features:
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</P>
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<UL><LI>The package is designed to exploit common GPU hardware configurations
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where one or more GPUs are coupled with many cores of a multi-core
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CPUs, e.g. within a node of a parallel machine.
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<LI>Atom-based data (e.g. coordinates, forces) moves back-and-forth
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between the CPU(s) and GPU every timestep.
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<LI>Neighbor lists can be constructed on the CPU or on the GPU
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<LI>The charge assignement and force interpolation portions of PPPM can be
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run on the GPU. The FFT portion, which requires MPI communication
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between processors, runs on the CPU.
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<LI>Asynchronous force computations can be performed simultaneously on the
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CPU(s) and GPU.
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<LI>LAMMPS-specific code is in the GPU package. It makes calls to a
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generic GPU library in the lib/gpu directory. This library provides
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NVIDIA support as well as more general OpenCL support, so that the
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same functionality can eventually be supported on a variety of GPU
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hardware.
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</UL>
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<P><B>Hardware and software requirements:</B>
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</P>
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<P>To use this package, you currently need to have specific NVIDIA
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hardware and install specific NVIDIA CUDA software on your system:
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</P>
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<UL><LI>Check if you have an NVIDIA card: cat /proc/driver/nvidia/cards/0
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<LI>Go to http://www.nvidia.com/object/cuda_get.html
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<LI>Install a driver and toolkit appropriate for your system (SDK is not necessary)
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<LI>Follow the instructions in lammps/lib/gpu/README to build the library (see below)
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<LI>Run lammps/lib/gpu/nvc_get_devices to list supported devices and properties
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</UL>
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<P><B>Building LAMMPS with the GPU package:</B>
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</P>
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<P>As with other packages that include a separately compiled library, you
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need to first build the GPU library, before building LAMMPS itself.
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General instructions for doing this are in <A HREF = "doc/Section_start.html#start_3">this
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section</A> of the manual. For this
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package, do the following, using a Makefile in lib/gpu appropriate for
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your system:
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</P>
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<PRE>cd lammps/lib/gpu
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make -f Makefile.linux
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(see further instructions in lammps/lib/gpu/README)
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</PRE>
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<P>If you are successful, you will produce the file lib/libgpu.a.
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</P>
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<P>Now you are ready to build LAMMPS with the GPU package installed:
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</P>
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<PRE>cd lammps/src
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make yes-gpu
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make machine
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</PRE>
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<P>Note that the lo-level Makefile (e.g. src/MAKE/Makefile.linux) has
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these settings: gpu_SYSINC, gpu_SYSLIB, gpu_SYSPATH. These need to be
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set appropriately to include the paths and settings for the CUDA
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system software on your machine. See src/MAKE/Makefile.g++ for an
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example.
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</P>
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<P><B>GPU configuration</B>
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</P>
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<P>When using GPUs, you are restricted to one physical GPU per LAMMPS
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process, which is an MPI process running on a single core or
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processor. Multiple MPI processes (CPU cores) can share a single GPU,
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and in many cases it will be more efficient to run this way.
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</P>
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<P><B>Input script requirements:</B>
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</P>
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<P>Additional input script requirements to run pair or PPPM styles with a
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<I>gpu</I> suffix are as follows:
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</P>
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<UL><LI>To invoke specific styles from the GPU package, you can either append
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"gpu" to the style name (e.g. pair_style lj/cut/gpu), or use the
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<A HREF = "Section_start.html#start_6">-suffix command-line switch</A>, or use the
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<A HREF = "suffix.html">suffix</A> command.
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<LI>The <A HREF = "newton.html">newton pair</A> setting must be <I>off</I>.
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<LI>The <A HREF = "package.html">package gpu</A> command must be used near the beginning
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of your script to control the GPU selection and initialization
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settings. It also has an option to enable asynchronous splitting of
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force computations between the CPUs and GPUs.
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</UL>
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<P>As an example, if you have two GPUs per node and 8 CPU cores per node,
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and would like to run on 4 nodes (32 cores) with dynamic balancing of
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force calculation across CPU and GPU cores, you could specify
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</P>
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<PRE>package gpu force/neigh 0 1 -1
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</PRE>
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<P>In this case, all CPU cores and GPU devices on the nodes would be
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utilized. Each GPU device would be shared by 4 CPU cores. The CPU
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cores would perform force calculations for some fraction of the
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particles at the same time the GPUs performed force calculation for
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the other particles.
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</P>
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<P><B>Timing output:</B>
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</P>
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<P>As described by the <A HREF = "package.html">package gpu</A> command, GPU
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accelerated pair styles can perform computations asynchronously with
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CPU computations. The "Pair" time reported by LAMMPS will be the
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maximum of the time required to complete the CPU pair style
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computations and the time required to complete the GPU pair style
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computations. Any time spent for GPU-enabled pair styles for
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computations that run simultaneously with <A HREF = "bond_style.html">bond</A>,
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<A HREF = "angle_style.html">angle</A>, <A HREF = "dihedral_style.html">dihedral</A>,
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<A HREF = "improper_style.html">improper</A>, and <A HREF = "kspace_style.html">long-range</A>
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calculations will not be included in the "Pair" time.
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</P>
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<P>When the <I>mode</I> setting for the package gpu command is force/neigh,
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the time for neighbor list calculations on the GPU will be added into
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the "Pair" time, not the "Neigh" time. An additional breakdown of the
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times required for various tasks on the GPU (data copy, neighbor
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calculations, force computations, etc) are output only with the LAMMPS
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screen output (not in the log file) at the end of each run. These
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timings represent total time spent on the GPU for each routine,
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regardless of asynchronous CPU calculations.
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</P>
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<P><B>Performance tips:</B>
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</P>
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<P>Generally speaking, for best performance, you should use multiple CPUs
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per GPU, as provided my most multi-core CPU/GPU configurations.
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</P>
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<P>Because of the large number of cores within each GPU device, it may be
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more efficient to run on fewer processes per GPU when the number of
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particles per MPI process is small (100's of particles); this can be
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necessary to keep the GPU cores busy.
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</P>
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<P>See the lammps/lib/gpu/README file for instructions on how to build
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the GPU library for single, mixed, or double precision. The latter
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requires that your GPU card support double precision.
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</P>
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<HR>
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<HR>
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<H4><A NAME = "acc_4"></A>5.4 USER-CUDA package
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</H4>
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<P>The USER-CUDA package was developed by Christian Trott at U Technology
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Ilmenau in Germany. It provides NVIDIA GPU versions of many pair
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styles, many fixes, a few computes, and for long-range Coulombics via
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the PPPM command. It has the following features:
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</P>
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<UL><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.
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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
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<LI>Data will stay on the GPU until a timestep where a non-GPU-ized fix or
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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.
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<LI>Neighbor lists for GPU-ized pair styles are constructed on the
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GPU.
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<LI>The package only supports use of a single CPU (core) with each
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GPU.
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</UL>
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<P><B>Hardware and software requirements:</B>
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</P>
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<P>To use this package, you need to have specific NVIDIA hardware and
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install specific NVIDIA CUDA software on your system.
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</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:
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</P>
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<P>http://en.wikipedia.org/wiki/Comparison_of_Nvidia_graphics_processing_units
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|
</P>
|
|
<P>Install the Nvidia Cuda Toolkit in version 3.2 or higher and the
|
|
corresponding GPU drivers. The Nvidia Cuda SDK is not required for
|
|
LAMMPSCUDA but we recommend it be installed. You can then make sure
|
|
that its sample projects can be compiled without problems.
|
|
</P>
|
|
<P><B>Building LAMMPS with the USER-CUDA package:</B>
|
|
</P>
|
|
<P>As with other packages that include a separately compiled library, you
|
|
need to first build the USER-CUDA library, before building LAMMPS
|
|
itself. General instructions for doing this are in <A HREF = "doc/Section_start.html#start_3">this
|
|
section</A> of the manual. For this
|
|
package, do the following, using settings in the lib/cuda Makefiles
|
|
appropriate for your system:
|
|
</P>
|
|
<UL><LI>Go to the lammps/lib/cuda directory
|
|
|
|
<LI>If your <I>CUDA</I> toolkit is not installed in the default system directoy
|
|
<I>/usr/local/cuda</I> edit the file <I>lib/cuda/Makefile.common</I>
|
|
accordingly.
|
|
|
|
<LI>Type "make OPTIONS", where <I>OPTIONS</I> are one or more of the following
|
|
options. The settings will be written to the
|
|
<I>lib/cuda/Makefile.defaults</I> and used in the next step.
|
|
|
|
<PRE><I>precision=N</I> 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
|
|
<I>arch=M</I> to set GPU compute capability
|
|
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)
|
|
<I>prec_timer=0/1</I> to use hi-precision timers
|
|
0 = do not use them (default)
|
|
1 = use these timers
|
|
this is usually only useful for Mac machines
|
|
<I>dbg=0/1</I> to activate debug mode
|
|
0 = no debug mode (default)
|
|
1 = yes debug mode
|
|
this is only useful for developers
|
|
<I>cufft=1</I> to determine usage of CUDA FFT library
|
|
0 = no CUFFT support (default)
|
|
in the future other CUDA-enabled FFT libraries might be supported
|
|
</PRE>
|
|
<LI>Type "make" to build the library. If you are successful, you will
|
|
produce the file lib/libcuda.a.
|
|
</UL>
|
|
<P>Now you are ready to build LAMMPS with the USER-CUDA package installed:
|
|
</P>
|
|
<PRE>cd lammps/src
|
|
make yes-user-cuda
|
|
make machine
|
|
</PRE>
|
|
<P>Note that the LAMMPS build references the lib/cuda/Makefile.common
|
|
file to extract setting specific CUDA settings. So it is important
|
|
that you have first built the cuda library (in lib/cuda) using
|
|
settings appropriate to your system.
|
|
</P>
|
|
<P><B>Input script requirements:</B>
|
|
</P>
|
|
<P>Additional input script requirements to run styles with a <I>cuda</I>
|
|
suffix are as follows:
|
|
</P>
|
|
<UL><LI>To invoke specific styles from the USER-CUDA package, you can either
|
|
append "cuda" to the style name (e.g. pair_style lj/cut/cuda), or use
|
|
the <A HREF = "Section_start.html#start_6">-suffix command-line switch</A>, or use
|
|
the <A HREF = "suffix.html">suffix</A> command. One exception is that the
|
|
<A HREF = "kspace_style.html">kspace_style pppm/cuda</A> command has to be requested
|
|
explicitly.
|
|
|
|
<LI>To use the USER-CUDA package with its default settings, no additional
|
|
command is needed in your input script. This is because when LAMMPS
|
|
starts up, it detects if it has been built with the USER-CUDA package.
|
|
See the <A HREF = "Section_start.html#start_6">-cuda command-line switch</A> for
|
|
more details.
|
|
|
|
<LI>To change settings for the USER-CUDA package at run-time, the <A HREF = "package.html">package
|
|
cuda</A> command can be used near the beginning of your
|
|
input script. See the <A HREF = "package.html">package</A> command doc page for
|
|
details.
|
|
</UL>
|
|
<P><B>Performance tips:</B>
|
|
</P>
|
|
<P>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.
|
|
</P>
|
|
<P>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.
|
|
</P>
|
|
<HR>
|
|
|
|
<HR>
|
|
|
|
<H4><A NAME = "acc_5"></A>5.5 Comparison of GPU and USER-CUDA packages
|
|
</H4>
|
|
<P>Both the GPU and USER-CUDA 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><B>Guidelines for using each package optimally:</B>
|
|
</P>
|
|
<UL><LI>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.
|
|
|
|
<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>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.
|
|
|
|
<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>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.
|
|
</UL>
|
|
<P><B>Differences between the two packages:</B>
|
|
</P>
|
|
<UL><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>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.
|
|
</UL>
|
|
<P><B>Examples:</B>
|
|
</P>
|
|
<P>The LAMMPS distribution has two directories with sample input scripts
|
|
for the GPU and USER-CUDA packages.
|
|
</P>
|
|
<UL><LI>lammps/examples/gpu = GPU package files
|
|
<LI>lammps/examples/USER/cuda = USER-CUDA package files
|
|
</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>
|
|
<HR>
|
|
|
|
<P><B>Benchmark data:</B>
|
|
</P>
|
|
<P>NOTE: We plan to add some benchmark results and plots here for the
|
|
examples described in the previous section.
|
|
</P>
|
|
<P>Simulations:
|
|
</P>
|
|
<P>1. Lennard Jones
|
|
</P>
|
|
<UL><LI>256,000 atoms
|
|
<LI>2.5 A cutoff
|
|
<LI>0.844 density
|
|
</UL>
|
|
<P>2. Lennard Jones
|
|
</P>
|
|
<UL><LI>256,000 atoms
|
|
<LI>5.0 A cutoff
|
|
<LI>0.844 density
|
|
</UL>
|
|
<P>3. Rhodopsin model
|
|
</P>
|
|
<UL><LI>256,000 atoms
|
|
<LI>10A cutoff
|
|
<LI>Coulomb via PPPM
|
|
</UL>
|
|
<P>4. Lihtium-Phosphate
|
|
</P>
|
|
<UL><LI>295650 atoms
|
|
<LI>15A cutoff
|
|
<LI>Coulomb via PPPM
|
|
</UL>
|
|
<P>Hardware:
|
|
</P>
|
|
<P>Workstation:
|
|
</P>
|
|
<UL><LI>2x GTX470
|
|
<LI>i7 950@3GHz
|
|
<LI>24Gb DDR3 @ 1066Mhz
|
|
<LI>CentOS 5.5
|
|
<LI>CUDA 3.2
|
|
<LI>Driver 260.19.12
|
|
</UL>
|
|
<P>eStella:
|
|
</P>
|
|
<UL><LI>6 Nodes
|
|
<LI>2xC2050
|
|
<LI>2xQDR Infiniband interconnect(aggregate bandwidth 80GBps)
|
|
<LI>Intel X5650 HexCore @ 2.67GHz
|
|
<LI>SL 5.5
|
|
<LI>CUDA 3.2
|
|
<LI>Driver 260.19.26
|
|
</UL>
|
|
<P>Keeneland:
|
|
</P>
|
|
<UL><LI>HP SL-390 (Ariston) cluster
|
|
<LI>120 nodes
|
|
<LI>2x Intel Westmere hex-core CPUs
|
|
<LI>3xC2070s
|
|
<LI>QDR InfiniBand interconnect
|
|
</UL>
|
|
</HTML>
|