Agent-based cellular models simulate tissue evolution by capturing the behavior of individual cells, their interactions with neighboring cells, and their responses to the surrounding microenvironment. An important challenge in the field is scaling cellular resolution models to real-scale tumor simulations, which is critical for the development of digital twin models of diseases and requires the use of High-Performance Computing (HPC) since every time step involves trillions of operations. We hereby present a scalable HPC solution for the molecular diffusion modeling using an efficient implementation of state-of-the-art Finite Volume Method (FVM) frameworks. The paper systematically evaluates a novel scalable Biological Finite Volume Method (BioFVM) library and presents an extensive performance analysis of the available solutions. Results shows that our HPC proposal reach almost 200x speedup and up to 36% reduction in memory usage over the current state-of-the-art solutions, paving the way to efficiently compute the next generation of biological problems.

翻译：基于智能体的细胞模型通过捕捉单个细胞的行为、细胞与邻近细胞的相互作用以及细胞对周围微环境的响应来模拟组织演化。该领域的一个重要挑战是将细胞分辨率模型扩展到真实尺度的肿瘤模拟，这对于疾病数字孪生模型的开发至关重要，且由于每个时间步涉及数万亿次运算，必须使用高性能计算。本文提出了一种用于分子扩散建模的可扩展高性能计算解决方案，该方案通过高效实现最先进的有限体积法框架实现。本文系统评估了新型可扩展生物有限体积法库，并对现有解决方案进行了全面的性能分析。结果表明，相较于当前最先进的解决方案，我们的高性能计算方案实现了近200倍的加速比，内存使用量降低高达36%，为高效计算下一代生物学问题铺平了道路。