remove references to Make.py and USER-CUDA
This commit is contained in:
Axel Kohlmeyer
@ -1,55 +1,21 @@
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These are input scripts used to run versions of several of the
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benchmarks in the top-level bench directory using the GPU and
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USER-CUDA accelerator packages. The results of running these scripts
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on two different machines (a desktop with 2 Tesla GPUs and the ORNL
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Titan supercomputer) are shown on the "GPU (Fermi)" section of the
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Benchmark page of the LAMMPS WWW site: lammps.sandia.gov/bench.
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benchmarks in the top-level bench directory using the GPU accelerator
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package. The results of running these scripts on two different machines
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(a desktop with 2 Tesla GPUs and the ORNL Titan supercomputer) are shown
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on the "GPU (Fermi)" section of the Benchmark page of the LAMMPS WWW
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site: lammps.sandia.gov/bench.
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Examples are shown below of how to run these scripts. This assumes
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you have built 3 executables with both the GPU and USER-CUDA packages
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you have built 3 executables with the GPU package
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installed, e.g.
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lmp_linux_single
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lmp_linux_mixed
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lmp_linux_double
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The precision (single, mixed, double) refers to the GPU and USER-CUDA
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package precision. See the README files in the lib/gpu and lib/cuda
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directories for instructions on how to build the packages with
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different precisions. The GPU and USER-CUDA sub-sections of the
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doc/Section_accelerate.html file also describes this process.
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Make.py -d ~/lammps -j 16 -p #all orig -m linux -o cpu -a exe
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Make.py -d ~/lammps -j 16 -p #all opt orig -m linux -o opt -a exe
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Make.py -d ~/lammps -j 16 -p #all omp orig -m linux -o omp -a exe
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Make.py -d ~/lammps -j 16 -p #all gpu orig -m linux \
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-gpu mode=double arch=20 -o gpu_double -a libs exe
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Make.py -d ~/lammps -j 16 -p #all gpu orig -m linux \
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-gpu mode=mixed arch=20 -o gpu_mixed -a libs exe
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Make.py -d ~/lammps -j 16 -p #all gpu orig -m linux \
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-gpu mode=single arch=20 -o gpu_single -a libs exe
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Make.py -d ~/lammps -j 16 -p #all cuda orig -m linux \
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-cuda mode=double arch=20 -o cuda_double -a libs exe
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Make.py -d ~/lammps -j 16 -p #all cuda orig -m linux \
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-cuda mode=mixed arch=20 -o cuda_mixed -a libs exe
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Make.py -d ~/lammps -j 16 -p #all cuda orig -m linux \
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-cuda mode=single arch=20 -o cuda_single -a libs exe
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Make.py -d ~/lammps -j 16 -p #all intel orig -m linux -o intel_cpu -a exe
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Make.py -d ~/lammps -j 16 -p #all kokkos orig -m linux -o kokkos_omp -a exe
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Make.py -d ~/lammps -j 16 -p #all kokkos orig -kokkos cuda arch=20 \
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-m cuda -o kokkos_cuda -a exe
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Make.py -d ~/lammps -j 16 -p #all opt omp gpu cuda intel kokkos orig \
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-gpu mode=double arch=20 -cuda mode=double arch=20 -m linux \
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-o all -a libs exe
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Make.py -d ~/lammps -j 16 -p #all opt omp gpu cuda intel kokkos orig \
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-kokkos cuda arch=20 -gpu mode=double arch=20 \
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-cuda mode=double arch=20 -m cuda -o all_cuda -a libs exe
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------------------------------------------------------------------------
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To run on just CPUs (without using the GPU or USER-CUDA styles),
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To run on just CPUs (without using the GPU styles),
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do something like the following:
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mpirun -np 1 lmp_linux_double -v x 8 -v y 8 -v z 8 -v t 100 < in.lj
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@ -81,23 +47,5 @@ node via a "-ppn" setting.
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------------------------------------------------------------------------
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To run with the USER-CUDA package, do something like the following:
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mpirun -np 1 lmp_linux_single -c on -sf cuda -v x 16 -v y 16 -v z 16 -v t 100 < in.lj
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mpirun -np 2 lmp_linux_double -c on -sf cuda -pk cuda 2 -v x 32 -v y 64 -v z 64 -v t 100 < in.eam
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The "xyz" settings determine the problem size. The "t" setting
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determines the number of timesteps. The "np" setting determines how
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many MPI tasks (per node) the problem will run on. The numeric
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argument to the "-pk" setting is the number of GPUs (per node); 1 GPU
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is the default. Note that the number of MPI tasks must equal the
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number of GPUs (both per node) with the USER-CUDA package.
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These mpirun commands run on a single node. To run on multiple nodes,
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scale up the "-np" setting, and control the number of MPI tasks per
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node via a "-ppn" setting.
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------------------------------------------------------------------------
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If the script has "titan" in its name, it was run on the Titan
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supercomputer at ORNL.
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46
bench/README
46
bench/README
@ -71,49 +71,33 @@ integration
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----------------------------------------------------------------------
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Here is a src/Make.py command which will perform a parallel build of a
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LAMMPS executable "lmp_mpi" with all the packages needed by all the
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examples. This assumes you have an MPI installed on your machine so
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that "mpicxx" can be used as the wrapper compiler. It also assumes
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you have an Intel compiler to use as the base compiler. You can leave
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off the "-cc mpi wrap=icc" switch if that is not the case. You can
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also leave off the "-fft fftw3" switch if you do not have the FFTW
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(v3) installed as an FFT package, in which case the default KISS FFT
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library will be used.
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cd src
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Make.py -j 16 -p none molecule manybody kspace granular rigid orig \
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-cc mpi wrap=icc -fft fftw3 -a file mpi
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----------------------------------------------------------------------
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Here is how to run each problem, assuming the LAMMPS executable is
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named lmp_mpi, and you are using the mpirun command to launch parallel
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runs:
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Serial (one processor runs):
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lmp_mpi < in.lj
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lmp_mpi < in.chain
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lmp_mpi < in.eam
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lmp_mpi < in.chute
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lmp_mpi < in.rhodo
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lmp_mpi -in in.lj
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lmp_mpi -in in.chain
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lmp_mpi -in in.eam
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lmp_mpi -in in.chute
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lmp_mpi -in in.rhodo
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Parallel fixed-size runs (on 8 procs in this case):
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mpirun -np 8 lmp_mpi < in.lj
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mpirun -np 8 lmp_mpi < in.chain
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mpirun -np 8 lmp_mpi < in.eam
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mpirun -np 8 lmp_mpi < in.chute
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mpirun -np 8 lmp_mpi < in.rhodo
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mpirun -np 8 lmp_mpi -in in.lj
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mpirun -np 8 lmp_mpi -in in.chain
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mpirun -np 8 lmp_mpi -in in.eam
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mpirun -np 8 lmp_mpi -in in.chute
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mpirun -np 8 lmp_mpi -in in.rhodo
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Parallel scaled-size runs (on 16 procs in this case):
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mpirun -np 16 lmp_mpi -var x 2 -var y 2 -var z 4 < in.lj
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mpirun -np 16 lmp_mpi -var x 2 -var y 2 -var z 4 < in.chain.scaled
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mpirun -np 16 lmp_mpi -var x 2 -var y 2 -var z 4 < in.eam
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mpirun -np 16 lmp_mpi -var x 4 -var y 4 < in.chute.scaled
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mpirun -np 16 lmp_mpi -var x 2 -var y 2 -var z 4 < in.rhodo.scaled
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mpirun -np 16 lmp_mpi -var x 2 -var y 2 -var z 4 -in in.lj
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mpirun -np 16 lmp_mpi -var x 2 -var y 2 -var z 4 -in in.chain.scaled
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mpirun -np 16 lmp_mpi -var x 2 -var y 2 -var z 4 -in in.eam
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mpirun -np 16 lmp_mpi -var x 4 -var y 4 -in in.chute.scaled
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mpirun -np 16 lmp_mpi -var x 2 -var y 2 -var z 4 -in in.rhodo.scaled
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For each of the scaled-size runs you must set 3 variables as -var
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command line switches. The variables x,y,z are used in the input
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@ -105,20 +105,11 @@ tad: temperature-accelerated dynamics of vacancy diffusion in bulk Si
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vashishta: models using the Vashishta potential
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voronoi: Voronoi tesselation via compute voronoi/atom command
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Here is a src/Make.py command which will perform a parallel build of a
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LAMMPS executable "lmp_mpi" with all the packages needed by all the
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examples, with the exception of the accelerate sub-directory. See the
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accelerate/README for Make.py commands suitable for its example
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scripts.
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cd src
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Make.py -j 16 -p none std no-lib reax meam poems reaxc orig -a lib-all mpi
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Here is how you might run and visualize one of the sample problems:
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cd indent
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cp ../../src/lmp_mpi . # copy LAMMPS executable to this dir
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lmp_mpi < in.indent # run the problem
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lmp_mpi -in in.indent # run the problem
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Running the simulation produces the files {dump.indent} and
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{log.lammps}. You can visualize the dump file as follows:
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@ -1,14 +1,11 @@
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These are example scripts that can be run with any of
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the acclerator packages in LAMMPS:
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USER-CUDA, GPU, USER-INTEL, KOKKOS, USER-OMP, OPT
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GPU, USER-INTEL, KOKKOS, USER-OMP, OPT
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The easiest way to build LAMMPS with these packages
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is via the src/Make.py tool described in Section 2.4
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of the manual. You can also type "Make.py -h" to see
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its options. The easiest way to run these scripts
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is by using the appropriate
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is via the flags described in Section 4 of the manual.
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The easiest way to run these scripts is by using the appropriate
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Details on the individual accelerator packages
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can be found in doc/Section_accelerate.html.
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@ -16,21 +13,6 @@ can be found in doc/Section_accelerate.html.
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Build LAMMPS with one or more of the accelerator packages
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The following command will invoke the src/Make.py tool with one of the
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command-lines from the Make.list file:
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../../src/Make.py -r Make.list target
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target = one or more of the following:
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cpu, omp, opt
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cuda_double, cuda_mixed, cuda_single
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gpu_double, gpu_mixed, gpu_single
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intel_cpu, intel_phi
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kokkos_omp, kokkos_cuda, kokkos_phi
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If successful, the build will produce the file lmp_target in this
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directory.
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Note that in addition to any accelerator packages, these packages also
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need to be installed to run all of the example scripts: ASPHERE,
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MOLECULE, KSPACE, RIGID.
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@ -38,39 +20,11 @@ MOLECULE, KSPACE, RIGID.
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These two targets will build a single LAMMPS executable with all the
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CPU accelerator packages installed (USER-INTEL for CPU, KOKKOS for
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OMP, USER-OMP, OPT) or all the GPU accelerator packages installed
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(USER-CUDA, GPU, KOKKOS for CUDA):
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(GPU, KOKKOS for CUDA):
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target = all_cpu, all_gpu
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Note that the Make.py commands in Make.list assume an MPI environment
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exists on your machine and use mpicxx as the wrapper compiler with
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whatever underlying compiler it wraps by default. If you add "-cc mpi
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wrap=g++" or "-cc mpi wrap=icc" after the target, you can choose the
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underlying compiler for mpicxx to invoke. E.g.
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../../src/Make.py -r Make.list intel_cpu -cc mpi wrap=icc
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You should do this for any build that includes the USER-INTEL
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package, since it will perform best with the Intel compilers.
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Note that for kokkos_cuda, it needs to be "-cc nvcc" instead of "mpi",
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since a KOKKOS for CUDA build requires NVIDIA nvcc as the wrapper
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compiler.
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Also note that the Make.py commands in Make.list use the default
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FFT support which is via the KISS library. If you want to
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build with another FFT library, e.g. FFTW3, then you can add
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"-fft fftw3" after the target, e.g.
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../../src/Make.py -r Make.list gpu -fft fftw3
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For any build with USER-CUDA, GPU, or KOKKOS for CUDA, be sure to set
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For any build with GPU, or KOKKOS for CUDA, be sure to set
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the arch=XX setting to the appropriate value for the GPUs and Cuda
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environment on your system. What is defined in the Make.list file is
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arch=21 for older Fermi GPUs. This can be overridden as follows,
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e.g. for Kepler GPUs:
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../../src/Make.py -r Make.list gpu_double -gpu mode=double arch=35
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environment on your system.
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---------------------
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@ -118,12 +72,6 @@ Note that when running in.lj.5.0 (which has a long cutoff) with the
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GPU package, the "-pk tpa" setting should be > 1 (e.g. 8) for best
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performance.
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** USER-CUDA package
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lmp_machine -c on -sf cuda < in.lj
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mpirun -np 1 lmp_machine -c on -sf cuda < in.lj # 1 MPI, 1 MPI/GPU
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mpirun -np 2 lmp_machine -c on -sf cuda -pk cuda 2 < in.lj # 2 MPI, 1 MPI/GPU
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** KOKKOS package for OMP
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lmp_kokkos_omp -k on t 1 -sf kk -pk kokkos neigh half < in.lj
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