Toward cardiac electrophysiology digital twins with an efficient open source scalable solver on GPU clusters

Modelling and simulation are essential in biomedicine, and specifically in computational cardiology. Reliable, efficient and accurate solvers are critical. This study presents an open-source, GPU-based cardiac electrophysiology solver for scalable multiscale simulations (monoalg3d), incorporating conduction system calibration and performance optimization. The solver employs the monodomain equation coupled with the Purkinje network, solved via the finite volume method, featuring a GPU-based linear solver and concurrent simulation dispatch with MPI. We demonstrate a $$10.94\times$$ speedup over a CPU-based solution and scalability by running 512 simulations on 128 compute nodes. Coarse and fine biventricular mesh simulations with 855, 670 and 6, 845, 360 control volumes are completed in less than 24 min and 303 min, respectively, considering a single beat and a human-based ventricular cellular model with 43 state variables. The proposed open-source solver enhances computational efficiency and physiological fidelity through Purkinje-muscle-junction calibration, enabling large-scale, high-speed cardiac simulations including the conduction system. This work marks a significant step toward fast and scalable cardiac simulations on GPU architectures by providing execution of concurrent simulations with the novel MPI batch feature and calibration of Purkinje coupling parameters, paving the way for integration into a Digital Twin personalisation pipeline, including the conduction system.