doc/expose/expose.tex

@@ -64,7 +64,6 @@ From an implementor's perspective, the \acrshort{cpm} has a great advantage over
 Since updates happen on a square lattice and changes in energy can be calculated locally, it lends itself well to distributed programming.
 \acrfull{cis}~\cite{berghoff2020} is a parallel implementation of the \acrshort{cpm} based on the \acrfull{nastja} framework~\cite{berghoff2018}.
 \acrshort{nastja} offers an abstraction layer for implementing stencil codes on the \acrfull{mpi}, making it possible to leverage large-scale parallelism for \acrshort{cis}.
-\acrshort{nastja} divides the domain into blocks, computing each stencil locally, then performing an halo exchanges of the border regions.
 
 \subsection{The \acrfull{ecm}}
 
@@ -87,7 +86,7 @@ We focus on approaches that explicitly model the plasticity of \acrshort{ecm} co
 
 A starting point is to model the \acrshort{ecm} as a static cell.
 In this model, a cell ID is chosen to represent the solid parts of the \acrshort{ecm}.
-Cell-matrix interactions are regulated by the Hamiltonian just like cell-cell interactions.
+Cell-matrix interactions are regulated by the hamiltonian just like cell-cell interactions.
 \acrshort{ecm} lattice sites do not copy their neighbors and can not be copied by their neighbors during a \acrshort{mcs}.
 Instead, simulations using this approach usually allow cells to degrade adjacent matrix sites over time.
 This approach is used for example in~\cite{bauer2007}, where the \acrshort{ecm} is initialized by randomly placing fiber bundles across the domain and~\cite{scianna2013}, which investigates cell behavior in \acrshortpl{ecm} with regular patterns.
@@ -96,13 +95,13 @@ This approach is used for example in~\cite{bauer2007}, where the \acrshort{ecm}
 
 An approach using a \acrfull{fem} is presented in~\cite{vanoers2014} and expanded upon in~\cite{rens2017, rens2019}.
 Each lattice site is assigned a local directional strain on the \acrshort{ecm}.
-Cells exert traction forces on the \acrshort{ecm} used to calculate the lattice strains by a \acrshort{fem}.
+Cells exert a traction forces on the \acrshort{ecm} used to calculate the lattice strains by a \acrshort{fem}.
 The hamiltonian of the \acrshort{cpm} is modified such that cells respond to the strain.
 
 \paragraph{Hybrid \acrshort{cpm} and Molecular Dynamics Methods}
 
 Another approach is presented in~\cite{tsingos2022}.
-This work simulates matrix fibers using a bead-and-chain model.
+This work models simulates matrix fibers using a bead-and-chain model.
 Similar to the previous approach, the \acrshort{ecm} model is coupled with the \acrshort{cpm}.
 However, in this work, cells interact with the \acrshort{ecm} only through a sparse subset of lattice sites.
 
@@ -126,56 +125,23 @@ In order to align the viscoelastic \acrshort{ecm} model with the \acrshort{cpm},
 
 A model for viscoelastic solids is presented in~\cite{obrien2008}.
 This work extends the discrete particle method for elastic solids presented in~\cite{toomey2000}.
-It is based on a two- or three-dimensional square lattice of particles.
+This model is based on a two- or three-dimensional square lattice of particles.
-Each particle is connected to all of its cardinal and diagonal neighbors.
+Each particle is connected to all of its Moore neighbors.
 The model for the force acting between two particles can be elastic or viscoelastic.
 Various models are explored in~\cite{obrien2008, obrien2009, obrien2014, obrien2021}.
 
 \paragraph{\acrfull{lbm}}
 
-The \acrshort{lbm} is an established approach for modeling the dynamics of fluids~\cite{krueger2017}.
+The \acrshort{lbm} is an established approach for modeling the dynamics of fluids.~\cite{krueger2017}
 Also based on a square lattice, this model discretizes the particles moving at a particular lattice space into the cardinal and diagonal directions.
 Research suggests that the \acrshort{lbm} can be used for modeling both solids~\cite{maquart2022} and viscoelastic fluids~\cite{malaspinas2010}.
-Perhaps for this particular use case, a \acrshort{lbm} could be configured to model the \acrshort{ecm}.
 
 \section{Contribution}
 
-In this work we will explore lattice-based viscoelastic simulations of the \acrshort{ecm} in the \acrshort{cpm}.
-
 \subsection{Method}
 
-For the \acrshort{cpm} we use the distributed implementation \acrshort{cis}.
-\acrshort{cis} is based on the \acrshort{nastja} framework implemented using the \acrshort{mpi}, which we will use to develop our model of the \acrshort{ecm}.
-The model will likely be based on the viscoelastic discrete particle method, as preliminary experiments based on a simplified implementation show promising results.
-
-In order to model cell-matrix interactions, we will develop a method that allows cells to influence the \acrshort{ecm} simulation.
-To model matrix-cell interactions, we will expand the Hamiltonian of the \acrshort{cpm} to include a term dependent on the local configuration of the \acrshort{ecm}.
-This should make it possible for our model to simulate the self-reinforcing interactions of cells and \acrshort{ecm}.
-
 \subsection{Challenges}
 
-Our preliminary experiments have produced some questions and likely challenges that our work will need to address.
-
-\paragraph{Spatial Scale}
-
-While we could simply use the same lattice for the \acrshort{ecm} model as for the \acrshort{cpm}, it is not clear that this will deliver the best results.
-It could be useful to use a scaled lattice, e.g.\ where the lattice spacing of the \acrshort{ecm} model is twice as long.
-
-\paragraph{Temporal Scale}
-
-Compared to cells, the waves in a viscoelastic material move quickly.
-It is likely that our model of the \acrshort{ecm} will have to go through multiple time steps between the \acrshortpl{mcs} of the \acrshort{cpm}.
-In the context of \acrshort{nastja}, this means an increased number of halo exchanges between ranks per \acrshort{mcs}.
-In order to reduce the number of halo exchanges, we could increase the width of the halo which allows the \acrshort{ecm} simulation to run for multiple time steps between halo exchanges.
-As this approach necessarily leads to diminishing returns as the halo data gets bigger, an efficient configuration needs to be investigated.
-
-\paragraph{Implementation Performance}
-
-As \acrshort{cis} is designed to large and therefore compute-heavy simulations, it is worthwhile to measure the and optimize the compute needed by our implementation.
-Since the discrete particle method is a dense approach, it should be possible to leverage common parallelization techniques such as vectorization and \acrshort{gpu} programming to improve performance.
-In particular, it might prove useful to run the \acrshort{cpm} on \acrshortpl{cpu} and the \acrshort{ecm} model of \acrshortpl{gpu}.
-We will experiment with these techniques and evaluate the possible improvements.
-
 \newpage
 
 \printglossary[type=\acronymtype, nogroupskip]