These are example scripts that can be run with any of the acclerator packages in LAMMPS:

USER-CUDA, GPU, USER-INTEL, KOKKOS, USER-OMP, OPT

The easiest way to build LAMMPS with these packages is via the src/Make.py tool described in Section 2.4 of the manual. You can also type "Make.py -h" to see its options. The easiest way to run these scripts is by using the appropriate

Details on the individual accelerator packages can be found in doc/Section_accelerate.html.

Build LAMMPS with one or more of the accelerator packages

The following command will invoke the src/Make.py tool with one of the command-lines from the Make.list file:

../../src/Make.py -r Make.list target

target = one or more of the following:

cpu, omp, opt
cuda_double, cuda_mixed, cuda_single
gpu_double, gpu_mixed, gpu_single
intel_cpu, intel_phi
kokkos_omp, kokkos_cuda, kokkos_phi

If successful, the build will produce the file lmp_target in this directory.

Note that in addition to any accelerator packages, these packages also need to be installed to run all of the example scripts: ASPHERE, MOLECULE, KSPACE, RIGID.

These two targets will build a single LAMMPS executable with all the CPU accelerator packages installed (USER-INTEL for CPU, KOKKOS for OMP, USER-OMP, OPT) or all the GPU accelerator packages installed (USER-CUDA, GPU, KOKKOS for CUDA):

target = all_cpu, all_gpu

Note that the Make.py commands in Make.list assume an MPI environment exists on your machine and use mpicxx as the wrapper compiler with whatever underlying compiler it wraps by default. If you add "-cc mpi wrap=g++" or "-cc mpi wrap=icc" after the target, you can choose the underlying compiler for mpicxx to invoke. E.g.

../../src/Make.py -r Make.list intel_cpu -cc mpi wrap=icc

You should do this for any build that includes the USER-INTEL package, since it will perform best with the Intel compilers.

Note that for kokkos_cuda, it needs to be "-cc nvcc" instead of "mpi", since a KOKKOS for CUDA build requires NVIDIA nvcc as the wrapper compiler.

Also note that the Make.py commands in Make.list use the default FFT support which is via the KISS library. If you want to build with another FFT library, e.g. FFTW3, then you can add "-fft fftw3" after the target, e.g.

../../src/Make.py -r Make.list gpu -fft fftw3

For any build with USER-CUDA, GPU, or KOKKOS for CUDA, be sure to set the arch=XX setting to the appropriate value for the GPUs and Cuda environment on your system. What is defined in the Make.list file is arch=21 for older Fermi GPUs. This can be overridden as follows, e.g. for Kepler GPUs:

../../src/Make.py -r Make.list gpu_double -gpu mode=double arch=35

Running with each of the accelerator packages

All of the input scripts have a default problem size and number of timesteps:

in.lj = LJ melt with cutoff of 2.5 = 32K atoms for 100 steps in.lj.5.0 = same with cutoff of 5.0 = 32K atoms for 100 steps in.phosphate = 11K atoms for 100 steps in.rhodo = 32K atoms for 100 steps in.lc = 33K atoms for 100 steps (after 200 steps equilibration)

These can be reset using the x,y,z and t variables in the command line. E.g. adding "-v x 2 -v y 2 -v z 4 -t 1000" to any of the run command below would run a 16x larger problem (2x2x4) for 1000 steps.

Here are example run commands using each of the accelerator packages:

• CPU only

lmp_cpu < in.lj mpirun -np 4 lmp_cpu -in in.lj

• OPT package

lmp_opt -sf opt < in.lj mpirun -np 4 lmp_opt -sf opt -in in.lj

• USER-OMP package

lmp_omp -sf omp -pk omp 1 < in.lj mpirun -np 4 lmp_omp -sf opt -pk omp 1 -in in.lj # 4 MPI, 1 thread/MPI mpirun -np 2 lmp_omp -sf opt -pk omp 4 -in in.lj # 2 MPI, 4 thread/MPI

• GPU package

lmp_gpu_double -sf gpu < in.lj mpirun -np 8 lmp_gpu_double -sf gpu < in.lj # 8 MPI, 8 MPI/GPU mpirun -np 12 lmp_gpu_double -sf gpu -pk gpu 2 < in.lj # 12 MPI, 6 MPI/GPU mpirun -np 4 lmp_gpu_double -sf gpu -pk gpu 2 tpa 8 < in.lj.5.0 # 4 MPI, 2 MPI/GPU

Note that when running in.lj.5.0 (which has a long cutoff) with the GPU package, the "-pk tpa" setting should be > 1 (e.g. 8) for best performance.

• USER-CUDA package

lmp_machine -c on -sf cuda < in.lj mpirun -np 1 lmp_machine -c on -sf cuda < in.lj # 1 MPI, 1 MPI/GPU mpirun -np 2 lmp_machine -c on -sf cuda -pk cuda 2 < in.lj # 2 MPI, 1 MPI/GPU

• KOKKOS package for OMP

lmp_kokkos_omp -k on t 1 -sf kk -pk kokkos neigh half < in.lj mpirun -np 2 lmp_kokkos_omp -k on t 4 -sf kk < in.lj # 2 MPI, 4 thread/MPI

Note that when running with just 1 thread/MPI, "-pk kokkos neigh half" was speficied to use half neighbor lists which are faster when running on just 1 thread.

• KOKKOS package for CUDA

lmp_kokkos_cuda -k on t 1 -sf kk < in.lj # 1 thread, 1 GPU mpirun -np 2 lmp_kokkos_cuda -k on t 6 g 2 -sf kk < in.lj # 2 MPI, 6 thread/MPI, 1 MPI/GPU

• KOKKOS package for PHI

mpirun -np 1 lmp_kokkos_phi -k on t 240 -sf kk -in in.lj # 1 MPI, 240 threads/MPI mpirun -np 30 lmp_kokkos_phi -k on t 8 -sf kk -in in.lj # 30 MPI, 8 threads/MPI

• USER-INTEL package for CPU

lmp_intel_cpu -sf intel < in.lj mpirun -np 4 lmp_intl_cpu -sf intel < in.lj # 4 MPI mpirun -np 4 lmp_intl_cpu -sf intel -pk omp 2 < in.lj # 4 MPI, 2 thread/MPI

• USER-INTEL package for PHI

lmp_intel_phi -sf intel -pk intel 1 omp 16 < in.lc # 1 MPI, 16 CPU thread/MPI, 1 Phi, 240 Phi thread/MPI mpirun -np 4 lmp_intel_phi -sf intel -pk intel 1 omp 2 < in.lc # 4 MPI, 2 CPU threads/MPI, 1 Phi, 60 Phi thread/MPI

Note that there is currently no Phi support for pair_style lj/cut in the USER-INTEL package.