CADISHI -- CAlculation of DIStance HIstograms -- is a software package that enables scientists to compute (Euclidean) distance histograms efficiently. Any sets of objects that have 3D Cartesian coordinates may be used as input, for example, atoms in molecular dynamics datasets or galaxies in astrophysical contexts. CADISHI drives the high-performance kernels pydh (CPU) and cudh (GPU, optional) to do the actual histogram computation. The kernels pydh and cudh are part of CADISHI and are written in C++ and CUDA.
For more information, we refer to our publication:
K. Reuter, J. Koefinger; CADISHI: Fast parallel calculation of particle-pair distance histograms on CPUs and GPUs; Comp. Phys. Comm. (236), 274 (2019).
A preprint of the paper is available on arXiv.org.
The CADISHI package is installed in the canonical way e.g. as follows:
python setup.py install --userThe setup_install_local.sh script may be used to perform the local installation. Make sure to add $HOME/.local/bin to your PATH environment variable.
CADISHI was developed, built, and tested on SUSE Linux Enterprise Server 11 and 12, Ubuntu Linux 14.04 LTS and 16.04 LTS, and Scientific Linux 7 using the Anaconda Python distribution version 2, release 4.0.0, and newer. CADISHI requires gcc, and optionally nvcc, to compile C++ code during the installation. CADISHI was successfully tested Mac computers as well.
CADISHI provides a single executable cadishi that gives access to the distance histogram calculations. Run cadishi --help to get an overview on the available commands and options.
To run an example calculation based on the data set included in CADISHI proceed as follows:
- Run cadishi example to generate an example input file histograms.yaml.
- Optional: Adapt the file histograms.yaml.
- Run cadishi histo to run the distance histogram calculation.
Note that the input data needs to be prepared in HDF5 format for performance reasons. See the included example dataset for details. The histograms are written to an HDF5 file as well. CADISHI uses multiple processes to be able to utilize all the compute resources (CPU cores, GPUs) available on a node simultaneously.
Documentation is available at http://cadishi.readthedocs.io/en/latest/. Alternatively, you may access the local copy at doc/html/index.html after having cloned the repository.
The CADISHI software is designed and packaged as a Python package. Each Python source file contains docstrings, explaining each module and function. Nevertheless, an explanation of the directory structure and of important files therein is given in the following.
- cadishi directory, and subdirectories
CADISHI Python module, implementing various functions necessary to run the CADISHI engine.
- cadishi/exe directory
CADISHI executables, to be invoked via the cadishi command. Most importantly, histograms.py is the main program of the CADISHI engine.
- cadishi/kernel directory, and subdirectories
C++/CUDA high-performance implementations of the distance histogram computation, and interfaces to Python. Noteworthy files are:
- cadishi/kernel/c_pydh.pyx
Cython interface to the CPU distance histogram kernel (pydh).
- cadishi/kernel/c_pydh_functions.cc
C++ CPU distance histogram kernel implementation.
- cadishi/kernel/c_cudh.pyx
Cython interface to the GPU distance histogram kernel (cudh).
- cadishi/kernel/c_cudh_functions.cu
CUDA GPU distance histogram kernel.
- doc directory
Documentation in rst format, to be processed using the Sphinx documentation system.
- aux directory
Auxiliary scripts for CADISHI, e.g. bash completion and data import.
- scripts directory
Example installation scripts for CADISHI.
The CADISHI package is released under the permissive MIT license. See the file LICENSE.txt for details.
Copyright 2015-2019 Klaus Reuter (MPCDF), Juergen Koefinger (MPIBP)
In case you're using CADISHI for own academic or non-academic research, we kindly request that you cite CADISHI in your publications and presentations. We suggest the following citation as appropriate:
K. Reuter, J. Koefinger; CADISHI: Fast parallel calculation of particle-pair distance histograms on CPUs and GPUs; Computer Physics Communications (2018); <https://doi.org/10.1016/j.cpc.2018.10.018>.