cellgpu
GPU-accelerated simulations of Voronoi and vertex models of cells. Initial version published in Computer Physics Communications: https://doi.org/10.1016/j.cpc.2017.06.001
Science Score: 54.0%
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Low similarity (16.7%) to scientific vocabulary
Repository
GPU-accelerated simulations of Voronoi and vertex models of cells. Initial version published in Computer Physics Communications: https://doi.org/10.1016/j.cpc.2017.06.001
Basic Info
Statistics
- Stars: 22
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- Open Issues: 0
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Metadata Files
README.md
CellGPU
Changes in progress
I am currently in the process of updating the cellGPU codebase --- its age is showing, and it has become a bit unweildy to maintain. Please use the v1.0.0 tag for legacy code and compilation, as it is the last commit before this modernization effort.
Current versions will start requiring c++-17, a more modern build system, and newer versions of cuda. I'll be updating the structure of the code base to better reflect best practices in building a simulation framework. Progress will be slow, since I'm not a postdoc anymore.
Information
CellGPU implements GPU-accelerated algorithms to simulate off-lattice models of cells. Its current two main feature sets focus on a Voronoi-decomposition-based model of two-dimensional monolayers and on a two-dimensional dynamical version of the vertex model. CellGPU grew out of DMS' "DelGPU" and "VoroGuppy" projects, and the current class structure still bears some traces of that (please see the contributing page of the documentation, which is maintained at https://dmsussman.gitlab.io/cellGPUdocumentation for information on upcoming code refactoring and new planned features). The paper describing this code in more detail can currently be found on the arXiv (https://arxiv.org/abs/1702.02939), or in print ( http://www.sciencedirect.com/science/article/pii/S0010465517301832)
Information on installing the project and contributing to it is contained in the relevant markdown files in the base directory and in the doc/markdown directory. Documentation of the code is maintained via Doxygen, which can be viewed at the gitlab.io pagea linked to above, or by compiling the doxygen documentation in the "/doc" directory
A very rough outline of some of the main classes and the basic operating flow of the primary branches of the code can be found here; this page is a good place to start before diving into the code (Please note that if you are reading this on the Gitlab main page the links will not work... visit the main documentation page at https://dmsussman.gitlab.io/cellGPUdocumentation or compile Doxygen documentation locally).
By default cellGPU includes a few different classes, mostly using the hdf5 format, for saving simulation data. For convenience, a few Mathematica scripts demonstrating how to load these files and turn them into simple visualizations are included in the visualizationTools directory -- these should be readily portable to matlab, python, etc.
Project information
Here are some convenient links to a variety of general information about the cellGPU project; all of the below can also be accessed from the @ref projectInfo tab (links work on the gitlab.io documenation website)
Owner
- Name: Sussman Lab
- Login: sussmanLab
- Kind: organization
- Website: https://www.dmsussman.org/
- Repositories: 2
- Profile: https://github.com/sussmanLab
Soft matter physics @ Emory
Citation (CITATIONS.md)
# Citations for cellGPU {#cite}
If you use cellGPU for a publication or project, please cite the main cellGPU paper:
(1) "cellGPU: massively parallel simulations of dynamic vertex models" Daniel M. Sussman; Computer Physics Communications, volume 219, pages 400-406, (2017)
Here are some additional citation to consider, according to what parts of the code you use and your
taste on how much to cite:
(2) Chen and Gotsman ''Localizing the delaunay triangulation and its parallel implementation,''
[Transactions on Computational Science XX (M. L. Gavrilova, C.J.K. Tan, and B. Kalantari, eds.),Lecture Notes in Computer Science, vol. 8110, Springer Berlin Heidelberg, 2013, Extended abstract in ISVD 2012, pp. 24–31, pp. 39–55 (English)]
The local ''test-and-repair'' part of the code used in the SPV branch is parallelized using an idea
from this paper. In particular, it points out a locality condition for the Delaunay neighborhood of a given point
(Given a polygon formed by other vertices that encloses the target point, the possible set of Delaunay
neighbors of the target point are those points contained in any of the circumcircles that can be
formed by that point and consecutive vertices of the polygon).
(3) CGAL,Computational Geometry Algorithms Library, http://www.cgal.org
(4) [E. Bitzek et al. Phys. Rev. Lett. 97, 170201 (2006)](http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.97.170201)
One of the energy minimizer uses a straightforward implementation of the FIRE minimization algorithm,
which is described in the above paper.
(5) [G. J. Martyna, M. E. Tuckerman, D. J. Tobias, and M. L. Klein; Mol. Phys. 87, 1117 (1996)](http://www.tandfonline.com/doi/abs/10.1080/00268979600100761)
The NoseHooverChainNVT class integrates the Nose-Hoover equations of motion with a chain of thermostats,
and does so using an update scheme that is explicitly time-reversible. The algorithm to do this is
described in Martyna et al., (see also the nice algorithmic pseudo-code in the Frenkel & Smit textbook)
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