A high-performance dynamic block activation framework for continuum models

Efficient utilization of massively parallel computing resources is crucial for advancing scientific understanding through complex simulations. However, existing adaptive methods often face challenges in implementation complexity and limited scalability on modern parallel hardware. In this work, we present Dynamic Block Activation (DBA), a novel, portable, and conceptually simple acceleration method that can be applied to a broad range of continuum simulations by strategically allocating computational resources based on the dynamic features of the physical model. By exploiting the hierarchical structure of parallel hardware and dynamically activating and deactivating computation blocks, DBA optimizes performance while maintaining accuracy. We demonstrate DBA's effectiveness through solving representative models spanning multiple scientific fields, including materials science, biophysics, and fluid dynamics, achieving substantial speedups compared to serial code in the tested cases. By addressing common challenges such as divergent memory access and reducing programming burden, DBA offers a promising approach to fully leverage massively parallel systems.