Skip to main content

High performance distance histogram calculation framework for CPUs and GPUs

Project description

CADISHI

Introduction

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.

Documentation

Documentation is available at http://cadishi.readthedocs.io/en/latest/ <http://cadishi.readthedocs.io/en/latest/>. Alternatively, you may access the local copy at [doc/html/index.html]{.title-ref} after having cloned the repository.

License and Citation

The CADISHI package is released under the permissive MIT license. See the file [LICENSE.txt]{.title-ref} 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>.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for cadishi, version 1.1.3
Filename, size File type Python version Upload date Hashes
Filename, size cadishi-1.1.3.tar.gz (4.3 MB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page