ArticleInternational journal for numerical methods in biomedical engineering2014
Toward GPGPU accelerated human electromechanical cardiac simulations.
Article in International journal for numerical methods in biomedical engineering, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Parallelization of Three Dimensional Cardiac Simulation on GPU.Biomedicines · 2024Article
- A memory optimization method combined with adaptive time-step method for cardiac cell simulation based on multi-GPU.Medical & biological engineering & computing · 2020Article
- Accelerating simulations of cardiac electrical dynamics through a multi-GPU platform and an optimized data structure.Concurrency and computation : practice & experience · 2020Article
- Interpreting Activation Mapping of Atrial Fibrillation: A Hybrid Computational/Physiological Study.Annals of biomedical engineering · 2018Article
- Multiphysics and multiscale modelling, data-model fusion and integration of organ physiology in the clinic: ventricular cardiac mechanics.Interface focus · 2016Review
- Parallel Optimization of 3D Cardiac Electrophysiological Model Using GPU.Computational and mathematical methods in medicine · 2015Article
- A spatially-distributed computational model to quantify behaviour of contrast agents in MR perfusion imaging.Medical image analysis · 2014Article
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Authors and funding
6 authors.
Funding
Abstract
In this paper, we look at the acceleration of weakly coupled electromechanics using the graphics processing unit (GPU). Specifically, we port to the GPU a number of components of CHeart--a CPU-based finite element code developed for simulating multi-physics problems. On the basis of a criterion of computational cost, we implemented on the GPU the ODE and PDE solution steps for the electrophysiology problem and the Jacobian and residual evaluation for the mechanics problem. Performance of the GPU implementation is then compared with single core CPU (SC) execution as well as multi-core CPU (MC) computations with equivalent theoretical performance. Results show that for a human scale left ventricle mesh, GPU acceleration of the electrophysiology problem provided speedups of 164 × compared with SC and 5.5 times compared with MC for the solution of the ODE model. Speedup of up to 72 × compared with SC and 2.6 × compared with MC was also observed for the PDE solve. Using the same human geometry, the GPU implementation of mechanics residual/Jacobian computation provided speedups of up to 44 × compared with SC and 2.0 × compared with MC.
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