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7.4.1. GPU package

The GPU package was developed by Mike Brown while at SNL and ORNL (now at Intel Corp.) and his collaborators, particularly Trung Nguyen (now at Northwestern). Support for AMD GPUs via HIP was added by Vsevolod Nikolskiy and coworkers at HSE University.

The GPU package provides GPU versions of many pair styles and for parts of the kspace_style pppm for long-range Coulombics. It has the following general features:

• It is designed to exploit common GPU hardware configurations where one or more GPUs are coupled to many cores of one or more multicore CPUs, e.g. within a node of a parallel machine.

• Atom-based data (e.g. coordinates, forces) are moved back-and-forth between the CPU(s) and GPU every timestep.

• Neighbor lists can be built on the CPU or on the GPU

• The charge assignment 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.

• Force computations of different style (pair vs. bond/angle/dihedral/improper) can be performed concurrently on the GPU and CPU(s), respectively.

• 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.

• LAMMPS-specific code is in the GPU package. It makes calls to a generic GPU library in the lib/gpu directory. This library provides either Nvidia support, AMD support, or more general OpenCL support (for Nvidia GPUs, AMD GPUs, Intel GPUs, and multicore CPUs). so that the same functionality is supported on a variety of hardware.

Required hardware/software:

To compile and use this package in CUDA mode, you currently need to have an NVIDIA GPU and install the corresponding NVIDIA CUDA toolkit software on your system (this is only tested on Linux and unsupported on Windows):

• Check if you have an NVIDIA GPU: cat /proc/driver/nvidia/gpus/\*/information

• Go to https://developer.nvidia.com/cuda-downloads

• 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

To compile and use this package in OpenCL mode, you currently need to have the OpenCL headers and the (vendor neutral) OpenCL library installed. In OpenCL mode, the acceleration depends on having an OpenCL Installable Client Driver (ICD) installed. There can be multiple of them for the same or different hardware (GPUs, CPUs, Accelerators) installed at the same time. OpenCL refers to those as ‘platforms’. The GPU library will try to auto-select the best suitable platform, but this can be overridden using the platform option of the package command. run lammps/lib/gpu/ocl_get_devices to get a list of available platforms and devices with a suitable ICD available.

To compile and use this package for Intel GPUs, OpenCL or the Intel oneAPI HPC Toolkit can be installed using linux package managers. The latter also provides optimized C++, MPI, and many other libraries and tools. See:

• https://software.intel.com/content/www/us/en/develop/tools/oneapi/hpc-toolkit/download.html

If you do not have a discrete GPU card installed, this package can still provide significant speedups on some CPUs that include integrated GPUs. Additionally, for many macs, OpenCL is already included with the OS and Makefiles are available in the lib/gpu directory.

To compile and use this package in HIP mode, you have to have the AMD ROCm software installed. Versions of ROCm older than 3.5 are currently deprecated by AMD.

Building LAMMPS with the GPU package:

See the Build extras page for instructions.

Run with the GPU package from the command line:

The mpirun or mpiexec command sets the total number of MPI tasks used by LAMMPS (one or multiple per compute node) and the number of MPI tasks used per node. E.g. the mpirun command in MPICH does this via its -np and -ppn switches. Ditto for OpenMPI via -np and -npernode.

When using the GPU package, you cannot assign more than one 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. Likewise it may be more efficient to use less MPI tasks/node than the available # of CPU cores. Assignment of multiple MPI tasks to a GPU will happen automatically if you create more MPI tasks/node than there are GPUs/mode. E.g. with 8 MPI tasks/node and 2 GPUs, each GPU will be shared by 4 MPI tasks.

The GPU package also has limited support for OpenMP for both multi-threading and vectorization of routines that are run on the CPUs. This requires that the GPU library and LAMMPS are built with flags to enable OpenMP support (e.g. -fopenmp). Some styles for time integration are also available in the GPU package. These run completely on the CPUs in full double precision, but exploit multi-threading and vectorization for faster performance.

Use the -sf gpu command-line switch, which will automatically append “gpu” to styles that support it. Use the -pk gpu Ng command-line switch to set Ng = # of GPUs/node to use. If Ng is 0, the number is selected automatically as the number of matching GPUs that have the highest number of compute cores.

# 1 MPI task uses 1 GPU
lmp_machine -sf gpu -pk gpu 1 -in in.script

# 12 MPI tasks share 2 GPUs on a single 16-core (or whatever) node
mpirun -np 12 lmp_machine -sf gpu -pk gpu 2 -in in.script

# ditto on 4 16-core nodes
mpirun -np 48 -ppn 12 lmp_machine -sf gpu -pk gpu 2 -in in.script

Note that if the -sf gpu switch is used, it also issues a default package gpu 0 command, which will result in automatic selection of the number of GPUs to use.

Using the -pk switch explicitly allows for setting of the number of GPUs/node to use and additional options. Its syntax is the same as the package gpu command. See the package command page for details, including the default values used for all its options if it is not specified.

Note that the default for the package gpu command is to set the Newton flag to “off” pairwise interactions. It does not affect the setting for bonded interactions (LAMMPS default is “on”). The “off” setting for pairwise interaction is currently required for GPU package pair styles.

Or run with the GPU package by editing an input script:

The discussion above for the mpirun or mpiexec command, MPI tasks/node, and use of multiple MPI tasks/GPU is the same.

Use the suffix gpu command, or you can explicitly add an “gpu” suffix to individual styles in your input script, e.g.

pair_style lj/cut/gpu 2.5

You must also use the package gpu command to enable the GPU package, unless the -sf gpu or -pk gpu command-line switches were used. It specifies the number of GPUs/node to use, as well as other options.

Speed-ups to expect:

The performance of a GPU versus a multicore 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). Using the GPU package in OpenCL mode on CPUs (which uses vectorization and multithreading) is usually resulting in inferior performance compared to using LAMMPS’ native threading and vectorization support in the OPENMP and INTEL packages.

See the Benchmark page of the LAMMPS website 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.

MPI parallelism typically outperforms OpenMP parallelism, but in some cases using fewer MPI tasks and multiple OpenMP threads with the GPU package can give better performance. 3-body potentials can often perform better with multiple OMP threads because the inter-process communication is higher for these styles with the GPU package in order to allow deterministic results.

Guidelines for best performance:

• Using multiple MPI tasks per GPU will often give the best performance, as allowed my most multicore CPU/GPU configurations.

• 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.

• The package gpu 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 package command page for details.

• As described by the package gpu 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, angle, dihedral, improper, and long-range calculations will not be included in the “Pair” time.

• Since only part of the pppm kspace style is GPU accelerated, it may be faster to only use GPU acceleration for Pair styles with long-range electrostatics. See the “pair/only” keyword of the package command for a shortcut to do that. The work between kspace on the CPU and non-bonded interactions on the GPU can be balanced through adjusting the coulomb cutoff without loss of accuracy.

• 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.

• 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.

Restrictions

None.