Draft:Highly Optimized Object-oriented Many-particle Dynamics
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HOOMD-blue (Highly Optimized Object-oriented Many-particle Dynamics—blue edition) is a general-purpose particle simulation engine designed primarily for nano- and colloidal-scale modeling. It serves the scientific purpose of probing the structure, dynamics, and self-assembly of complex matter. The software executes on single or multi-core central processing units (CPUs) as well as graphics processing unit (GPU) hardware architectures. Its principal research fields focus on soft matter physics, polymers, colloids, self-assembly, and active matter.[1] It is also enlisted in the list of different tools for molecular modelling in the table.
History and development
[edit]Development began in March 2007 at Iowa State University to explore the computational potential of graphics processing units for coarse-grained polymer simulations. The initial implementation achieved accelerated execution using C++ and CUDA code targeting single NVIDIA GPUs. In published benchmark evaluations, single-GPU execution produced computational performance comparable to approximately 30 CPU cores for equivalent benchmark systems.[2] In August 2008, HOOMD was released as open-source software. Shortly after, developers added a high-level Python scripting interface, transforming HOOMD-blue into an extensible Python package that integrates natively into the scientific Python ecosystem.[3]
Capabilities
[edit]HOOMD-blue supports molecular dynamics (MD) simulations in constant-volume (NVE, NVT) and constant-pressure (NPT, NPH) thermodynamic ensembles. For particle motion, the software provides separate equations of motion, including Langevin, Brownian, and overdamped dynamics. It evaluates interaction potentials such as short-range pair cutoffs, angular or dihedral force fields, embedded-atom method (EAM) potentials, and Finnis-Sinclair (FS) metallic terms.[4] Additionally, the Hard Particle Monte Carlo (HPMC) framework supports two-dimensional and three-dimensional simulations in constant-volume (NVT) and constant-pressure (NPT) ensembles.[5] Supported HPMC shape classes include disks, spheres, convex polygons, concave polyhedra, spheropolygons, and ellipsoids.[5] The high-level Python interface completely decouples simulation setup, particle initialization, and control loops from the low-level compiled backend.
Architecture and performance
[edit]The engine pairs a user-facing Python API with a compiled C++/CUDA backend optimized for hardware execution. While early versions were restricted to single-GPU workstations, the engine transitioned to distributed-memory execution using spatial MPI domain decomposition. This spatial decomposition divides the global simulation box across multiple parallel processor targets. Performance evaluations demonstrate scaling across parallel supercomputing architectures. For molecular dynamics, published benchmark studies tested simulations on up to 3,375 GPUs and systems containing as many as 108 million particles.[6] For Hard Particle Monte Carlo simulations, multi-GPU scaling tests demonstrated performance across up to 2,048 GPUs for systems containing 16.8 million hard polyhedra.[5]
Distribution and licensing
[edit]HOOMD-blue is distributed as open-source software under the permissive BSD 3-Clause licence, which permits free use, modification, and redistribution.[7] The source code repository, documentation, and user tutorials are publicly hosted online.[7] Researchers can extend the software by writing separately compiled C++ and Python plugins. This plugin system allows developers to introduce custom pair forces, novel particle shapes, constraint algorithms, and external potential fields without modifying the core codebase.
References
[edit]- ↑ Anderson, Joshua A.; Glaser, Jens; Glotzer, Sharon C. (2020-02-15). "HOOMD-blue: A Python package for high-performance molecular dynamics and hard particle Monte Carlo simulations". Computational Materials Science. 173 109363. arXiv:1308.5587. doi:10.1016/j.commatsci.2019.109363. ISSN 0927-0256.
- ↑ Anderson, Joshua A.; Lorenz, Chris D.; Travesset, A. (2008-05-01). "General purpose molecular dynamics simulations fully implemented on graphics processing units". Journal of Computational Physics. 227 (10): 5342–5359. Bibcode:2008JCoPh.227.5342A. doi:10.1016/j.jcp.2008.01.047. ISSN 0021-9991.
- ↑ Butler, Brandon L.; Ramasubramani, Vyas; Anderson, Joshua A.; Glotzer, Sharon C. (2020-06-01). "HOOMD-blue version 3.0 A Modern, Extensible, Flexible, Object-Oriented API for Molecular Simulations". SciPy 2020. Proceedings of the 19th Python in Science Conference: 24–31. doi:10.25080/Majora-342d178e-004.
- ↑ Yang, Lin; Zhang, Feng; Wang, Cai-Zhuang; Ho, Kai-Ming; Travesset, Alex (2018-04-15). "Implementation of metal-friendly EAM/FS-type semi-empirical potentials in HOOMD-blue: A GPU-accelerated molecular dynamics software". Journal of Computational Physics. 359: 352–360. Bibcode:2018JCoPh.359..352Y. doi:10.1016/j.jcp.2018.01.015. ISSN 0021-9991. OSTI 1548813.
- 1 2 3 Anderson, Joshua A.; Eric Irrgang, M.; Glotzer, Sharon C. (2016-07-01). "Scalable Metropolis Monte Carlo for simulation of hard shapes". Computer Physics Communications. 204: 21–30. arXiv:1509.04692. Bibcode:2016CoPhC.204...21A. doi:10.1016/j.cpc.2016.02.024. ISSN 0010-4655.
- ↑ Glaser, Jens; Nguyen, Trung Dac; Anderson, Joshua A.; Lui, Pak; Spiga, Filippo; Millan, Jaime A.; Morse, David C.; Glotzer, Sharon C. (July 2015). "Strong scaling of general-purpose molecular dynamics simulations on GPUs". Computer Physics Communications. 192: 97–107. arXiv:1412.3387. Bibcode:2015CoPhC.192...97G. doi:10.1016/j.cpc.2015.02.028. ISSN 0010-4655.
- 1 2 "License - HOOMD-blue 7.1.2 documentation". hoomd-blue.readthedocs.io. Retrieved 2026-08-30.