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

Abstract:
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 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.
Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Sub department:
Computer Science
Role:
Author


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Funder identifier:
10.13039/100010434
Grant:
LCF/BQ/PI25/12100029
More from this funder
Funder identifier:
10.13039/100010666
Grant:
823712
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Funder identifier:
10.13039/501100004901
Grant:
APQ-00748-18
More from this funder
Funder identifier:
10.13039/501100003593
Grant:
446127/2024-8
More from this funder
Funder identifier:
https://ror.org/029chgv08
Grant:
214290/Z/18/Z)


Publisher:
Nature Research
Journal:
Scientific Reports More from this journal
Volume:
16
Issue:
1
Article number:
9619
Publication date:
2026-02-18
Acceptance date:
2025-12-22
DOI:
EISSN:
2045-2322
ISSN:
2045-2322


Language:
English
Keywords:
Source identifiers:
3877475
Deposit date:
2026-03-23
ARK identifier:
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