7.4.1. GPU package

The GPU package was developed by Mike Brown while at SNL and ORNL and his collaborators, particularly Trung Nguyen (now at Northwestern). It 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 multi-core 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 NVIDIA support as well as more general OpenCL support, 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 primarily tested on Linux and completely unsupported on Windows):

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

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 select the first suitable platform, but this can be overridden using the device 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.

Building LAMMPS with the GPU package:

See the Build extras doc 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.

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.

lmp_machine -sf gpu -pk gpu 1 -in in.script                         # 1 MPI task uses 1 GPU
mpirun -np 12 lmp_machine -sf gpu -pk gpu 2 -in in.script           # 12 MPI tasks share 2 GPUs on a single 16-core (or whatever) node
mpirun -np 48 -ppn 12 lmp_machine -sf gpu -pk gpu 2 -in in.script   # ditto on 4 16-core nodes

Note that if the “-sf gpu” switch is used, it also issues a default package gpu 1 command, which sets the number of GPUs/node to 1.

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 same as the “package gpu” command. See the package command doc 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/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 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). 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 USER-OMP and USER-INTEL packages.

See the Benchmark page 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.
  • 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 doc 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.
  • 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.