Combined particle and compartmental dynamics of cell-biological models using hollow spheres on the GPU

Köster, Till (2017) Combined particle and compartmental dynamics of cell-biological models using hollow spheres on the GPU. Masters thesis, Institut of Computer Science, University of Rostock.

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In this work the ML-Force method for spatial particle-based cell-biological simulation and the gHSX algorithm and implementation for the novel HSX collision problem are presented. After an overview of existing methods of spatial cell-biological simulation, it becomes clear that there is a need for a new simulation approach, as no existing spatial approach can combine all aspects of space in one unified method. This new approach combines forces, nesting, excluded volumes, changing sizes and arbitrary functions, which is new to the literature. This new approach is well founded in fundamental principles of thermodynamics and can build on a strong existing algorithmic base in molecular dynamics. A simple example model is also investigated. In the particle-based simulation of cell-biological systems in continuous space, a key performance bottleneck is the computation of all possible intersections between particles. This typically rely on solid sphere approaches for collision detection. Existing collision detection algorithms are foud not to be designed for spatial cell-biological modells' hollow spheres (that allow for nesting), because nearly all existing high performance parallel algorithms are focusing on solid sphere interactions. In this work the new problem of computing the intersections among arbitrarily nested hollow spheres of possibly different sizes, thicknesses, positions, and nesting levels is defined. A new algorithm to solve this nested hollow sphere intersection problem is described and implemented for massively parallel execution on graphical processing units (GPUs). A performance study shows 1.8-4x improvements compared to existing GPU approaches.

Item Type: Thesis (Masters)
Projects: ESCeMMo