Abstract
Visualizing time-varying phenomena is paramount to ensure correct interpretation and analysis, provoke insights, and communicate those insights to others. In particular, visualization allows us the freedom to explore the spatial and temporal domains of such phenomena. The task of visualizing tissue growth is challenging due to the amount of data that needs to be visualized and the large simulation parameter space. Further, many problems and their solution strategies tend to be extremely heterogeneous: in their models, codes, and applications. Such solutions must be designed to manage this heterogeneity in an integrated way, so that the user is presented with a predictable and consistent computing environment. In this book chapter, the author presents an application of visualization to a three-dimensional simulation model for tissue growth. The chapter reports on the different components of the model where cellular automata is used to model populations of cells that execute persistent random walks on the computational grid, collide, and proliferate until they reach confluence. It discusses the main issues regarding the parallelization of the model and its implementation on a parallel machine. The author then elaborates on the integration of visualization with the said simulation model. This includes presenting the system architecture of the developed visualization solution and the employed rendering techniques. Finally, the chapter demonstrates some of the preliminary performance results and discusses the encountered challenges in this undertaking.