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The GPU package was developed by Mike Brown while at SNL and ORNLand his collaborators, particularly Trung Nguyen (now at Northwestern).It provides GPU versions of many pair styles and for parts of thekspace_style pppm for long-range Coulombics.It has the following general features:
It is designed to exploit common GPU hardware configurations where oneor 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-forthbetween 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 berun on the GPU. The FFT portion, which requires MPI communicationbetween 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 doubleprecision, or in mixed-mode precision, where pairwise forces arecomputed in single precision, but accumulated into double-precisionforce vectors.
LAMMPS-specific code is in the GPU package. It makes calls to ageneric GPU library in the lib/gpu directory. This library providesNVIDIA support as well as more general OpenCL support, so that thesame functionality is supported on a variety of hardware.
Required hardware/software:
To compile and use this package in CUDA mode, you currently needto have an NVIDIA GPU and install the corresponding NVIDIA CUDAtoolkit software on your system (this is primarily tested on Linuxand 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) tolist supported devices and properties
To compile and use this package in OpenCL mode, you currently needto 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 thoseas ‘platforms’. The GPU library will select the first suitable platform,but this can be overridden using the device option of the packagecommand. run lammps/lib/gpu/ocl_get_devices to get a list of availableplatforms and devices with a suitable ICD available.
To compute and use this package in HIP mode, you have to have the AMD ROCmsoftware installed. Versions of ROCm older than 3.5 are currently deprecatedby AMD.
Building LAMMPS with the GPU package:
See the Build extras doc page forinstructions.
Run with the GPU package from the command line:
The mpirun or mpiexec command sets the total number of MPI tasks usedby LAMMPS (one or multiple per compute node) and the number of MPItasks used per node. E.g. the mpirun command in MPICH does this viaits -np and -ppn switches. Ditto for OpenMPI via -np and -npernode.
When using the GPU package, you cannot assign more than one GPU to asingle MPI task. However multiple MPI tasks can share the same GPU,and in many cases it will be more efficient to run this way. Likewiseit may be more efficient to use less MPI tasks/node than the available# of CPU cores. Sign into my microsoft 365 account. Assignment of multiple MPI tasks to a GPU will happenautomatically if you create more MPI tasks/node than there areGPUs/mode. E.g. with 8 MPI tasks/node and 2 GPUs, each GPU will beshared by 4 MPI tasks.
Use the “-sf gpu” command-line switch, which willautomatically append “gpu” to styles that support it. Use the “-pkgpu Ng” command-line switch to set Ng = # ofGPUs/node to use.
Note that if the “-sf gpu” switch is used, it also issues a defaultpackage gpu 1 command, which sets the number ofGPUs/node to 1.
Using the “-pk” switch explicitly allows for setting of the number ofGPUs/node to use and additional options. Its syntax is the same asthe “package gpu” command. See the packagecommand doc page for details, including the default values used forall its options if it is not specified.
Note that the default for the package gpu command is toset the Newton flag to “off” pairwise interactions. It does notaffect the setting for bonded interactions (LAMMPS default is “on”).The “off” setting for pairwise interaction is currently required forGPU package pair styles.
Or run with the GPU package by editing an input script:
Gujarati bhajan lyrics in gujarati. 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.
You must also use the package gpu command to enable theGPU package, unless the “-sf gpu” or “-pk gpu” command-line switches were used. It specifies the number ofGPUs/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 yourhardware, which pair style is used, the number of atoms/GPU, and theprecision used on the GPU (double, single, mixed). Scrutiny 8 1 12 – suite of web optimization tools. Using the GPU packagein OpenCL mode on CPUs (which uses vectorization and multithreading) isusually resulting in inferior performance compared to using LAMMPS’ nativethreading and vectorization support in the USER-OMP and USER-INTEL packages.
See the Benchmark page of theLAMMPS web site for performance of the GPU package on varioushardware, including the Titan HPC platform at ORNL. Apple keyboard explained.
You should also experiment with how many MPI tasks per GPU to use togive the best performance for your problem and machine. This is alsoa function of the problem size and the pair style being using.Likewise, you should experiment with the precision setting for the GPUlibrary to see if single or mixed precision will give accurateresults, 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 ofparticles), it can be more efficient to run with fewer MPI tasks perGPU, even if you do not use all the cores on the compute node.
The package gpu command has several options for tuningperformance. Neighbor lists can be built on the GPU or CPU. Forcecalculations can be dynamically balanced across the CPU cores andGPUs. GPU-specific settings can be made which can be optimizedfor different hardware. See the package commanddoc page for details.
As described by the package gpu command, GPUaccelerated pair styles can perform computations asynchronously withCPU computations. The “Pair” time reported by LAMMPS will be themaximum of the time required to complete the CPU pair stylecomputations and the time required to complete the GPU pair stylecomputations. Any time spent for GPU-enabled pair styles forcomputations that run simultaneously with bond,angle, dihedral,improper, and long-rangecalculations 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 intothe “Pair” time, not the “Neigh” time. An additional breakdown of thetimes required for various tasks on the GPU (data copy, neighborcalculations, force computations, etc) are output only with the LAMMPSscreen output (not in the log file) at the end of each run. Thesetimings 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 datastorage by a single MPI process.
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