A parallel implementation of an off-lattice individual-based model of multicellular populations
Authored by James M Osborne, Alexander G Fletcher, Daniel G Harvey, Joe Pitt-Francis
Date Published: 2015
DOI: 10.1016/j.cpc.2015.03.005
Sponsors:
United Kingdom Engineering and Physical Sciences Research Council (EPSRC)
Microsoft Research Cambridge
Platforms:
C++
Model Documentation:
Other Narrative
Mathematical description
Model Code URLs:
Model code not found
Abstract
As computational models of multicellular populations include ever more
detailed descriptions of biophysical and biochemical processes, the
computational cost of simulating such models limits their ability to
generate novel scientific hypotheses and testable predictions. While
developments in microchip technology continue to increase the power of
individual processors, parallel computing offers an immediate increase
in available processing power. To make full use of parallel computing
technology, it is necessary to develop specialised algorithms. To this
end, we present a parallel algorithm for a class of off-lattice
individual-based models of multicellular populations. The algorithm
divides the spatial domain between computing processes and comprises
communication routines that ensure the model is correctly simulated on
multiple processors. The parallel algorithm is shown to accurately
reproduce the results of a deterministic simulation performed using a
pm-existing serial implementation. We test the scaling of computation
time, memory use and load balancing as more processes are used to
simulate a cell population of fixed size. We find approximate linear
scaling of both speed-up and memory consumption on up to 32 processor
cores. Dynamic load balancing is shown to provide speed-up for
non-regular spatial distributions of cells in the case of a growing
population. (C) 2015 Elsevier B.V. All rights reserved.
Tags
Simulation
Dynamics
systems
Tumor-growth
Adhesion
Colonic crypt
Monoclonal
conversion
Extended potts-model
Cell-based model
Growth in-vitro