forked from lijiext/lammps
186 lines
7.9 KiB
Plaintext
186 lines
7.9 KiB
Plaintext
--------------------------------
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LAMMPS ACCELERATOR LIBRARY
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--------------------------------
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W. Michael Brown (ORNL)
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Trung Dac Nguyen (ORNL/Northwestern)
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Peng Wang (NVIDIA)
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Axel Kohlmeyer (Temple)
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Steve Plimpton (SNL)
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Inderaj Bains (NVIDIA)
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-------------------------------------------------------------------
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This directory has source files to build a library that LAMMPS
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links against when using the GPU package.
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This library must be built with a C++ compiler, before LAMMPS is
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built, so LAMMPS can link against it.
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You can type "make lib-gpu" from the src directory to see help on how
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to build this library via make commands, or you can do the same thing
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by typing "python Install.py" from within this directory, or you can
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do it manually by following the instructions below.
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Build the library using one of the provided Makefile.* files or create
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your own, specific to your compiler and system. For example:
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make -f Makefile.linux
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When you are done building this library, two files should
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exist in this directory:
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libgpu.a the library LAMMPS will link against
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Makefile.lammps settings the LAMMPS Makefile will import
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Makefile.lammps is created by the make command, by copying one of the
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Makefile.lammps.* files. See the EXTRAMAKE setting at the top of the
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Makefile.* files.
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IMPORTANT: You should examine the final Makefile.lammps to insure it is
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correct for your system, else the LAMMPS build can fail.
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IMPORTANT: If you re-build the library, e.g. for a different precision
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(see below), you should do a "make clean" first, e.g. make -f
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Makefile.linux clean, to insure all previous derived files are removed
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before the new build is done.
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Makefile.lammps has settings for 3 variables:
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user-gpu_SYSINC = leave blank for this package
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user-gpu_SYSLIB = CUDA libraries needed by this package
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user-gpu_SYSPATH = path(s) to where those libraries are
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Because you have the CUDA compilers on your system, you should have
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the needed libraries. If the CUDA developement tools were installed
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in the standard manner, the settings in the Makefile.lammps.standard
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file should work.
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-------------------------------------------------------------------
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GENERAL NOTES
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--------------------------------
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This library, libgpu.a, provides routines for GPU acceleration
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of certain LAMMPS styles and neighbor list builds. Compilation of this
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library requires installing the CUDA GPU driver and CUDA toolkit for
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your operating system. Installation of the CUDA SDK is not necessary.
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In addition to the LAMMPS library, the binary nvc_get_devices will also
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be built. This can be used to query the names and properties of GPU
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devices on your system. A Makefile for OpenCL compilation is provided,
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but support for OpenCL use is not currently provided by the developers.
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Details of the implementation are provided in:
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----
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Brown, W.M., Wang, P. Plimpton, S.J., Tharrington, A.N. Implementing
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Molecular Dynamics on Hybrid High Performance Computers - Short Range
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Forces. Computer Physics Communications. 2011. 182: p. 898-911.
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and
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Brown, W.M., Kohlmeyer, A. Plimpton, S.J., Tharrington, A.N. Implementing
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Molecular Dynamics on Hybrid High Performance Computers - Particle-Particle
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Particle-Mesh. Computer Physics Communications. 2012. 183: p. 449-459.
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and
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Brown, W.M., Masako, Y. Implementing Molecular Dynamics on Hybrid High
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Performance Computers - Three-Body Potentials. Computer Physics Communications.
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2013. 184: p. 2785–2793.
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----
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NOTE: Installation of the CUDA SDK is not required, only the CUDA
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toolkit itself or an OpenCL 1.2 compatible header and library.
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Pair styles supporting GPU acceleration this this library
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are marked in the list of Pair style potentials with a "g".
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See the online version at: https://lammps.sandia.gov/doc/Commands_pair.html
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In addition the (plain) pppm kspace style is supported as well.
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MULTIPLE LAMMPS PROCESSES
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--------------------------------
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Multiple LAMMPS MPI processes can share GPUs on the system, but multiple
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GPUs cannot be utilized by a single MPI process. In many cases, the
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best performance will be obtained by running as many MPI processes as
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CPU cores available with the condition that the number of MPI processes
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is an integer multiple of the number of GPUs being used. See the
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LAMMPS user manual for details on running with GPU acceleration.
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BUILDING AND PRECISION MODES
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--------------------------------
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To build, edit the CUDA_ARCH, CUDA_PRECISION, CUDA_HOME variables in one of
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the Makefiles. CUDA_ARCH should be set based on the compute capability of
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your GPU. This can be verified by running the nvc_get_devices executable after
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the build is complete. Additionally, the GPU package must be installed and
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compiled for LAMMPS. This may require editing the gpu_SYSPATH variable in the
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LAMMPS makefile.
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Please note that the GPU library accesses the CUDA driver library directly,
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so it needs to be linked not only to the CUDA runtime library (libcudart.so)
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that ships with the CUDA toolkit, but also with the CUDA driver library
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(libcuda.so) that ships with the Nvidia driver. If you are compiling LAMMPS
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on the head node of a GPU cluster, this library may not be installed,
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so you may need to copy it over from one of the compute nodes (best into
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this directory). Recent CUDA toolkits starting from CUDA 9 provide a dummy
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libcuda.so library (typically under $(CUDA_HOME)/lib64/stubs), that can be used for
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linking.
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The gpu library supports 3 precision modes as determined by
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the CUDA_PRECISION variable:
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CUDA_PRECISION = -D_SINGLE_SINGLE # Single precision for all calculations
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CUDA_PRECISION = -D_DOUBLE_DOUBLE # Double precision for all calculations
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CUDA_PRECISION = -D_SINGLE_DOUBLE # Accumulation of forces, etc. in double
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As of CUDA 7.5 only GPUs with compute capability 2.0 (Fermi) or newer are
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supported and as of CUDA 9.0 only compute capability 3.0 (Kepler) or newer
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are supported. There are some limitations of this library for GPUs older
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than that, which require additional preprocessor flag, and limit features,
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but they are kept for historical reasons. There is no value in trying to
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use those GPUs for production calculations.
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You have to make sure that you set a CUDA_ARCH line suitable for your
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hardware and CUDA toolkit version: e.g. -arch=sm_35 for Tesla K20 or K40
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or -arch=sm_52 GeForce GTX Titan X. A detailed list of GPU architectures
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and CUDA compatible GPUs can be found e.g. here:
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https://en.wikipedia.org/wiki/CUDA#GPUs_supported
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NOTE: when compiling with CMake, all of the considerations listed below
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are considered within the CMake configuration process, so no separate
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compilation of the gpu library is required. Also this will build in support
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for all compute architecture that are supported by the CUDA toolkit version
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used to build the gpu library.
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Please note the CUDA_CODE settings in Makefile.linux_multi, which allows
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to compile this library with support for multiple GPUs. This list can be
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extended for newer GPUs with newer CUDA toolkits and should allow to build
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a single GPU library compatible with all GPUs that are worth using for
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GPU acceleration and supported by the current CUDA toolkits and drivers.
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NOTE: The system-specific setting LAMMPS_SMALLBIG (default), LAMMPS_BIGBIG,
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or LAMMPS_SMALLSMALL if specified when building LAMMPS (i.e. in
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src/MAKE/Makefile.foo) should be consistent with that specified
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when building libgpu.a (i.e. by LMP_INC in the lib/gpu/Makefile.bar).
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EXAMPLE CONVENTIONAL BUILD PROCESS
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--------------------------------
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cd ~/lammps/lib/gpu
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emacs Makefile.linux
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make -f Makefile.linux
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./nvc_get_devices
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cd ../../src
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emacs ./MAKE/Makefile.linux
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make yes-asphere
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make yes-kspace
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make yes-gpu
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make linux
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