HOOMD-blue: A Python package for high-performance molecular dynamics and hard particle Monte Carlo simulations

Joshua A. Anderson; Jens Glaser; Sharon C. Glotzer

doi:10.1016/j.commatsci.2019.109363

HOOMD-blue: A Python package for high-performance molecular dynamics and hard particle Monte Carlo simulations

Joshua A. Anderson, Jens Glaser, Sharon C. Glotzer

Research output: Contribution to journal › Article › peer-review

356 Scopus citations

Abstract

HOOMD-blue is a particle simulation engine designed for nano- and colloidal-scale molecular dynamics and hard particle Monte Carlo simulations. It has been actively developed since March 2007 and available open source since August 2008. HOOMD-blue is a Python package with a high performance C++/CUDA backend that we built from the ground up for GPU acceleration. The Python interface allows users to combine HOOMD-blue with other packages in the Python ecosystem to create simulation and analysis workflows. We employ software engineering practices to develop, test, maintain, and expand the code.

Original language	English
Article number	109363
Journal	Computational Materials Science
Volume	173
DOIs	https://doi.org/10.1016/j.commatsci.2019.109363
State	Published - Feb 15 2020
Externally published	Yes

Funding

Initial HOOMD development (v0.6-v0.8) was supervised by Alex Travesset and funded by the National Science Foundation through Grant DMR-0426597 and by DOE through the Ames lab under Contract No. DE-AC02-07CH11358. HOOMD-blue development has been supported by the DOD/ASD (R&E) under Award No. N00244-09-1-0062 (2009-2014, early design and implementation, v0.9 – v1.x) and the National Science Foundation, Division of Materials Research Award # DMR 1409620 (2014–2018, especially DEM and HPMC capabilities in v2.x). Software was validated and benchmarked on the Extreme Science and Engineering Discovery Environment (XSEDE) [58] , which is supported by National Science Foundation Grant No. ACI-1053575 (XSEDE award DMR 140129); on resources of the Oak Ridge Leadership Computing Facility which is a DOE Office of Science User Facility supported under Contract No. DE- AC05-00OR22725; and through computational resources and services provided by Advanced Research Computing at the University of Michigan, Ann Arbor. Hardware provided by NVIDIA Corp. is gratefully acknowledged. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the DOD/ASD(R&E). We would like to thank all HOOMD-blue contributors: Carl Simon Adorf, Khalid Ahmed, James Antonaglia, Steve Barr, Joseph Berleant, Isaac Bruss, Chengyu Dai, Kevin Daly, Avisek Das, Bradley Dice, Paul Dodd, Chrisy Du, Åsmund Ervik, Jenny Fothergill, Grey Garrett, Eric Harper, Mike Henry, Michael Howard, Alexander Hudson, M. Eric Irrgang, Eric Jankowski, Kwanghwi Je, Bjørnar Jensen, Christoph Junghans, Aaron Keys, Christoph Klein, Axel Kohlmeyer, Kevin Kohlstedt, David LeBard, Andrew Mark, Ryan Marson, Tim Moore, Shannon Moran, Igor Morozov, Pavani Medapuram Lakshmi Narasimha, Richmond Newman, Trung Dac Nguyen, Sam Nola, Antonio Osorio, Carolyn Phillips, James Proctor, Cong Qiao, Vyas Ramasubramani, Malcolm Ramsay, Sumedh R. Risbud, Luis Y. Rivera-Rivera, Ludwig Schneider, Benjamin Schultz, Peter Schwendeman, Wenbo Shen, Kevin Silmore, Rastko Sknepnek, Brandon Denis Smith, Ross Smith, Matthew Spellings, Ben Swerdlow, Erin Teich, Stephen Thomas, Alex Travesset, Alyssa Travitz, Greg van Anders, Bryan VanSaders, Lin Yang, Pengji Zhou, and William Zygmunt Initial HOOMD development (v0.6-v0.8) was supervised by Alex Travesset and funded by the National Science Foundation through Grant DMR-0426597 and by DOE through the Ames lab under Contract No. DE-AC02-07CH11358. HOOMD-blue development has been supported by the DOD/ASD (R&E) under Award No. N00244-09-1-0062 (2009-2014, early design and implementation, v0.9 ? v1.x) and the National Science Foundation, Division of Materials Research Award # DMR 1409620 (2014?2018, especially DEM and HPMC capabilities in v2.x). Software was validated and benchmarked on the Extreme Science and Engineering Discovery Environment (XSEDE) [58], which is supported by National Science Foundation Grant No. ACI-1053575 (XSEDE award DMR 140129); on resources of the Oak Ridge Leadership Computing Facility which is a DOE Office of Science User Facility supported under Contract No. DE- AC05-00OR22725; and through computational resources and services provided by Advanced Research Computing at the University of Michigan, Ann Arbor. Hardware provided by NVIDIA Corp. is gratefully acknowledged. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the DOD/ASD(R&E). We would like to thank all HOOMD-blue contributors: Carl Simon Adorf, Khalid Ahmed, James Antonaglia, Steve Barr, Joseph Berleant, Isaac Bruss, Chengyu Dai, Kevin Daly, Avisek Das, Bradley Dice, Paul Dodd, Chrisy Du, ?smund Ervik, Jenny Fothergill, Grey Garrett, Eric Harper, Mike Henry, Michael Howard, Alexander Hudson, M. Eric Irrgang, Eric Jankowski, Kwanghwi Je, Bj?rnar Jensen, Christoph Junghans, Aaron Keys, Christoph Klein, Axel Kohlmeyer, Kevin Kohlstedt, David LeBard, Andrew Mark, Ryan Marson, Tim Moore, Shannon Moran, Igor Morozov, Pavani Medapuram Lakshmi Narasimha, Richmond Newman, Trung Dac Nguyen, Sam Nola, Antonio Osorio, Carolyn Phillips, James Proctor, Cong Qiao, Vyas Ramasubramani, Malcolm Ramsay, Sumedh R. Risbud, Luis Y. Rivera-Rivera, Ludwig Schneider, Benjamin Schultz, Peter Schwendeman, Wenbo Shen, Kevin Silmore, Rastko Sknepnek, Brandon Denis Smith, Ross Smith, Matthew Spellings, Ben Swerdlow, Erin Teich, Stephen Thomas, Alex Travesset, Alyssa Travitz, Greg van Anders, Bryan VanSaders, Lin Yang, Pengji Zhou, and William Zygmunt

Funders	Funder number
DOD/ASD	2009-2014, N00244-09-1-0062, DMR 140129, ACI-1053575
DOE Office of Science	DE- AC05-00OR22725
DOE Office of Science User Facility supported
Division of Materials	DMR 1409620 (2014–2018
Extreme Science and Engineering Discovery Environment
Oak
William Zygmunt
XSEDE
National Science Foundation	1409620, DMR-0426597
U.S. Department of Energy
NVIDIA
Ames Laboratory	DE-AC02-07CH11358

Keywords

CUDA
GPU
Molecular dynamics
Molecular simulation
Monte Carlo
Python

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10.1016/j.commatsci.2019.109363

Cite this

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abstract = "HOOMD-blue is a particle simulation engine designed for nano- and colloidal-scale molecular dynamics and hard particle Monte Carlo simulations. It has been actively developed since March 2007 and available open source since August 2008. HOOMD-blue is a Python package with a high performance C++/CUDA backend that we built from the ground up for GPU acceleration. The Python interface allows users to combine HOOMD-blue with other packages in the Python ecosystem to create simulation and analysis workflows. We employ software engineering practices to develop, test, maintain, and expand the code.",

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doi = "10.1016/j.commatsci.2019.109363",

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HOOMD-blue: A Python package for high-performance molecular dynamics and hard particle Monte Carlo simulations

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Keywords

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