Power Consumption Comparison of GPU Linear Solvers for Cellular Potts Model Simulations

Abstract

Power consumption is a significant challenge in the sustainability of computational science. The growing energy demands of increasingly complex simulations and algorithms lead to substantial resource use, which conflicts with global sustainability goals. This paper investigates the energy efficiency of different parallel implementations of a Cellular Potts model, which models cellular behavior through Hamiltonian energy minimization techniques, leveraging modern GPU architectures. By evaluating alternative solvers, it demonstrates that specific methods can significantly enhance computational efficiency and reduce energy use compared to traditional approaches. The results confirm notable improvements in execution time and energy consumption. In particular, the experiments show a reduction in terms of power of up to 53%, providing a pathway towards more sustainable high-performance computing practices for complex biological simulations.