jglaser / neighbor

A flexible, templated CUDA library of neighbor search algorithms.

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neighbor

A library of CUDA implementations of neighbor search algorithms. Currently, the compressed LBVH and uniform grid are implemented. For additional details, please refer to the following publications:

Dependencies

  • HOOMD-blue >= 2.4.1
  • CMake >= 2.8.0
  • A CUDA toolkit compatible with HOOMD-blue and an NVIDIA GPU with compute capability >= 3.5.

Compiling

The neighbor library is intended to be compiled similarly to a HOOMD-blue plugin. First, you must install HOOMD-blue and its dependencies with CUDA enabled. Then, add your HOOMD-blue installation to your PYTHONPATH. The neighbor library should automatically find HOOMD-blue. From the current directory,

export PYTHONPATH=/path/to/hoomd/2.4.1
mkdir build && cd build
cmake ..

Set any CMake flags that are appropriate for your target architecture (e.g., CUDA_ARCH_LIST). Most options will have already been fixed by your HOOMD-blue installation. Then, simply compile and install,

make install

If you have difficulties with the CMake configuration, refer to the HOOMD-blue documentation for hints regarding compilation.

The following will be found in your installation:

  • bin: benchmark executables
  • include: header files for the neighbor library
  • lib: the neighbor library

Note that the neighbor library uses a fixed RPATH at installation. If you move your HOOMD-blue installation, you will need to recompile the library.

Testing

You can validate your installation using the included test suite. Make sure that you are on a machine that has at least one available GPU, and then run

make test

or a specific test using ctest.

Versioning

This library is currently under development, so APIs may change between all minor versions in 0.x. Stability of the API will be guaranteed between minor versions beginning with 1.x.

About

A flexible, templated CUDA library of neighbor search algorithms.

License:BSD 3-Clause "New" or "Revised" License


Languages

Language:C++ 63.4%Language:Cuda 31.3%Language:CMake 5.3%