Lessons Learned from a Performance Analysis and Optimization of a Multiscale Cellular Simulation

Abstract

This work presents a comprehensive performance analysis and optimization of a multiscale agent-based cellular simulation. The optimizations applied are guided by detailed performance analysis and include memory management, load balance, and a localityaware parallelization. The outcome of this paper is not only the speedup of 2.4x achieved by the optimized version with respect to the original PhysiCell code, but also the lessons learned and best practices when developing parallel HPC codes to obtain efficient and highly performant applications, especially in the computational biology field.

Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation

“…An aspect that requires consideration for the usage of mechanistic modeling and ML-assisted mechanistic modeling is ‘ scalability .’ Given that mechanistic modeling should be, for the sake of biological plausibility, based on local information exchange only, its simulation can naturally make use of parallelized and distributed computing. 101 , 102 For numerous ML methods, their adaptation for large-scale applications requires often custom efforts, since every algorithm has a distinct communication pattern, as demonstrated in the work of Verbraeken et al 103 Along those lines, synchronization requirements among nodes can vary across ML methods, as well as suitability for specific hardware (e.g., CPUs versus GPUs). Overall, the smooth and efficient interfacing with mechanistic modeling constitutes nevertheless a challenging task that remains to be addressed in the future.…”