Multiscale Parallel Simulation of Malignant Pleural Mesothelioma via Adaptive Domain Partitioning -- an Efficiency Analysis Study

TL;DR

This study presents a parallel simulation framework for malignant pleural mesothelioma growth, combining adaptive domain partitioning and efficient PDE solving to improve computational performance and resource utilization.

Contribution

It introduces an adaptive domain partitioning method for simulating tumor growth, enhancing efficiency and scalability of the computational model.

Findings

Parallelization reduces solving time compared to serial execution.

Adaptive partitioning optimizes memory and load balancing.

Efficient PDE solving accelerates tumor growth simulations.

Abstract

A novel parallel efficiency analysis on a framework for simulating the growth of Malignant Pleural Mesothelioma (MPM) tumours is presented. Proliferation of MPM tumours in the pleural space is simulated using a Cellular Potts Model (CPM) coupled with partial differential equations (PDEs). Using segmented lung data from CT scans, an environment is set up with artificial tumour data in the pleural space, representing the simulation domain, onto which a dynamic bounding box is applied to restrict computations to the region of interest, dramatically reducing memory and CPU overhead. This adaptive partitioning of the domain enables efficient use of computational resources by reducing the three-dimensional (3D) domain over which the PDEs are to be solved. The PDEs, representing oxygen, nutrients, and cytokines, are solved using the finite-volume method with a first-order implicit Euler scheme. Parallelization is realized using the public Python library mpi4py in combination with LinearGMRESSolver and PETSc for efficient convergence. Performance analyses have shown that parallelization achieves a reduced solving time compared to serial computation. Also, optimizations enable efficient use of available memory and improved load balancing amongst the cores.