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Positively Split Ewald (PSE)

PSE is a HOOMD plugin by Andrew M. Fiore containing a GPU implemention of the Positively Split Ewald (PSE) algorithm for calculation of the Rotne-Prager-Yamakawa (RPY) hydrodynamic mobility and stochastic thermal displacements. This repository is no longer maintained.

An alternative maintained GPU implementation can be found in UAMMD and is accessible using a simple python interface.

The theory behind the PSE method is described in the reference:

  1. Rapid Sampling of Stochastic Displacements in Brownian Dynamics Simulations, Andrew M. Fiore, Florencio Balboa Usabiaga, Aleksandar Donev, and James W. Swan, The Journal of Chemical Physics, 146, 124116 (2017).DOI arXiv

Files that come in this template

  • doc/TUTORIAL.pdf : a tutorial to use PSE.
  • CMakeLists.txt : main CMake configuration file for the plugin
  • FindHOOMD.cmake : script to find a HOOMD-Blue installation to link against
  • README : This file
  • PSEv1 : Directory containing C++ and CUDA source code that interacts with HOOMD. Also contains python UI level source code that drives the C++ module
  • cppmodule : Directory containing C++ and CUDA source code that interacts with HOOMD
  • examples/run.py : python example to use PSE.

Software requirements

The PSE plugin requires the following additional software:

  • HOOMD, compiled with CUDA (tested with version 2.3.3).
  • CUDA (tested with version 9.2).
  • LAPACKE (tested with version 3.6.1).
  • CBLAS (tested with version 3.6.1).

Software Installation

HOOMD can be installed following the instructions given in the documentation. HOOMD must be compiled with CUDA enabled. It is recommended to use the following cmake command

cmake ../ -DCMAKE_INSTALL_PREFIX=${SOFTWARE_ROOT}/lib/python -DCMAKE_CXX_FLAGS=-march=native -DCMAKE_C_FLAGS=-march=native -DENABLE_CUDA=ON -DENABLE_MPI=ON

where ${SOFTWARE_ROOT} is the path variable specifying the installation location for HOOMD.

LAPACKE and CBLAS can be install manually after downloading the source code from netlib and openblas or from repositorities. In Ubuntu, the simplest method is via repository:

sudo apt-get install liblapack3 liblapack-dev liblapacke liblapacke-dev
sudo apt-get install libblas3 libblas-dev libopenblas-dev libatlas-base-dev

Plugin Compilation

To compile this example plugin, follow steps similar to those in compiling HOOMD-Blue. The process of finding a HOOMD installation to link to will be fully automatic IF you have hoomd_install_dir/bin in your PATH when running cmake.

Note that plugins can only be built against a HOOMD build that has been installed via a package or compiled and then installed via 'make install'. HOOMD must be built with CUDA enabled -DENABLE_CUDA=ON in order for the package to work. Plugins can only be built against hoomd when it is built as a shared library.

From the root PSE folder do:

$ mkdir plugin_build
$ cd plugin_build
$ cmake ../
$ make -j6
$ make install

If hoomd is not in your PATH, you can specify the root using

$ cmake -DHOOMD_ROOT=/path/to/hoomd ../

You can also provide to cmake the location of LAPACKE, LAPACK, CBLAS, BLAS and the python version with the options

$ cmake -DHOOMD_ROOT=/path/to/hoomd  \
-DCBLAS_LIBRARIES=/path/to/cblas     \
-DBLAS_LIBRARIES=/path/to/blas       \
-DLAPACKE_LIBRARIES=/path/to/lapacke \
-DLAPACK_LIBRARIES=/path/to/lapack   \
-DPYTHON_EXECUTABLE=`which python`   \
../

however, these options are unecessary if these libraries have been installed into the standard directories.

By default, make install will install the plugin into

${HOOMD_ROOT}/lib/python/hoomd/PSEv1

This works if you have make installed hoomd into your home directory.

Using the Plugin

A sample script demonstrating how the plugin is used can be found in examples/run.py. You can call this script with the command

python3 run.py