GPU accelerated solver for nonlinear reaction–diffusion systems. Application to the electrophysiology problem

Mena, A.; Ferrero, J.M.; Matas, José Félix Rodríguez

doi:10.1016/j.cpc.2015.06.018

Cited by 24 publications

(15 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From the computational point of view, a deeper investigation of the CV in the circumferential direction using a more realistic gastric geometry should be accomplished, considering also the presence of the fundus, which is electrically quiescent but responsible for other, specific mechanical effects, namely the storage function of the stomach. Also, more efficient numerical schemes and GPU‐based codes are foreseen to speed‐up the wide numerical analyses necessary to fully characterize a complex organ like the stomach.…”

Section: Resultsmentioning

confidence: 99%

Computational model of gastric motility with active‐strain electromechanics

et al. 2018

View full text Add to dashboard Cite

We present an electro-mechanical constitutive framework for the modeling of gastric motility, including pacemaker electrophysiology and smooth muscle contractility. In this framework, we adopt a phenomenological description of the gastric tissue. Tissue electrophysiology is represented by a set of two minimal two-variable models and tissue electromechanics by an active-strain finite elasticity approach. We numerically investigate the implication of the spatial distribution of pacemaker cells on the entrainment and synchronization of the slow waves characterizing gastric motility in health and disease. On simple schematic model geometries, we demonstrate that the proposed computational framework is amenable to large scale in-silico analyses of the complex gastric motility including the underlying electro-mechanical coupling. K E Y W O R D Sactive-strain electro-mechanics, electrophysiology, finite elasticity, gastric motility Abbreviations: ICC, interstitial cell of Cajal; ICC-MY, myenteric interstitial cell of Cajal; SMC, smooth muscle cell; VDCC, voltage-dependent calcium channel; cpm, cycles per minute This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Section: Resultsmentioning

confidence: 99%

Computational model of gastric motility with active‐strain electromechanics

et al. 2018

View full text Add to dashboard Cite

show abstract

“…With a computation time for the prediction best expressed in seconds, we have demonstrated that the time required to obtain a steady state of the cell models can be reduced by at least two‐thirds and possibly even more than 90 % , thus further enabling the use of personalized EP simulations in the clinic, as well as studies on larger populations than previously have been used. One should also note that this approach was used in combination with parallelization of the simulations, and there should be no obstacle per se to prevent combination of our technique with GPU‐based acceleration such as those presented by Rocha et al and Mena et al…”

Section: Discussionmentioning

confidence: 99%

An atlas‐ and data‐driven approach to initializing reaction‐diffusion systems in computer cardiac electrophysiology

Hoogendoorn

Sebastián

Rodrı́guez

et al. 2016

Numer Methods Biomed Eng

View full text Add to dashboard Cite

The cardiac electrophysiology (EP) problem is governed by a nonlinear anisotropic reaction-diffusion system with a very rapidly varying reaction term associated with the transmembrane cell current. The nonlinearity associated with the cell models requires a stabilization process before any simulation is performed. More importantly, when used in a 3-dimensional (3D) anatomy, it is not sufficient to perform this stabilization on the basis of isolated cells only, since the coupling of the different cells through the tissue greatly modulates the dynamics of the system. Therefore, stabilization of the system must be performed on the entire 3D model. This work develops a novel procedure for the initialization of reaction-diffusion systems for numerical simulations of cardiac EP from steady-state conditions. We exploit surface point correspondence to establish volumetric point correspondence. Upon introduction of a new 3D anatomy with surface point correspondence, a prediction of the cell model steady states is derived from the set of earlier biophysical simulations. We show that the prediction error is typically less than 10% for all model variables, with most variables showing even greater accuracy. When initializing simulations with the predicted model states, it is demonstrated that simulation times can be cut by at least two-thirds and potentially more, which saves hours or days of high-performance computing. Overall, these results increase the clinical applicability of detailed computational EP studies on personalized anatomies.

show abstract

“…28,29 Cardiac electrical dynamics simulations is no exception to this trend. They evaluated not only the performance of adapted CPU implementations for GPUs, [33][34][35] but also different parallel strategies 11,20,[36][37][38][39] and numerical methods. They evaluated not only the performance of adapted CPU implementations for GPUs, [33][34][35] but also different parallel strategies 11,20,[36][37][38][39] and numerical methods.…”

Section: Related Workmentioning

confidence: 99%

“…[30][31][32] Several authors have tested GPU performance previously for different cardiac tissue models. They evaluated not only the performance of adapted CPU implementations for GPUs, [33][34][35] but also different parallel strategies 11,20,[36][37][38][39] and numerical methods. 40,41 Some authors have addressed the problem of implementing a solution for a PDE like Equation (3)…”

Section: Related Workmentioning

confidence: 99%

Accelerating simulations of cardiac electrical dynamics through a multi‐GPU platform and an optimized data structure

Vasconcellos

Clua

Fenton

et al. 2019

Concurrency and Computation

View full text Add to dashboard Cite

Simulations of cardiac electrophysiological models in tissue, particularly in 3D require the solutions of billions of differential equations even for just a couple of milliseconds, thus highly demanding in computational resources. In fact, even studies in small domains with very complex models may take several hours to reproduce seconds of electrical cardiac behavior.Today's Graphics Processor Units (GPUs) are becoming a way to accelerate such simulations, and give the added possibilities to run them locally without the need for supercomputers.Nevertheless, when using GPUs, bottlenecks related to global memory access caused by the spatial discretization of the large tissue domains being simulated, become a big challenge. For simulations in a single GPU, we propose a strategy to accelerate the computation of the diffusion term through a data-structure and memory access pattern designed to maximize coalescent memory transactions and minimize branch divergence, achieving results approximately 1.4 times faster than a standard GPU method. We also combine this data structure with a designed communication strategy to take advantage in the case of simulations in multi-GPU platforms.We demonstrate that, in the multi-GPU approach performs, simulations in 3D tissue can be just 4× slower than real time. KEYWORDScardiac electrophysiology models, GPU Computing, memory access optimization, parallel cardiac dynamics simulations INTRODUCTIONThe large increase of computational power over the last years shifted the bottleneck of different algorithms to the memory bandwidth and memory management. 1 One typical solution employed by hardware assemblers to minimize this issue is hardware hierarchical memory and memory locality optimization.Computational systems organize hierarchical memory system into levels. In the on-chip level, the registers are the fastest memory, with a high cost per byte and low capacity. Next, there are different cache levels according to the hardware architecture, typically called L1, L2, and so on.The main memory is the next level; here, the cost per byte is less than cache or registers, but latency is high. The last level is the secondary memory that has the highest latency with the lowest cost per byte. Overall, the cost per byte of each level determines the capacity and latency, which directly impact in performance.As each level of the hierarchical memory system has a different storage capacity and data is usually kept at the lowest memory level, computational systems must choose for each level which data will be prioritized to stay in memory and which will be removed when that memory level fills up. To do so, the computer memory system employs two fundamental principles, ie, temporal and spatial locality. 2 In general, these strategies aim to keep the most recently used data in the same memory level, since having to access higher memory levels drastically increases the time of the search.Based on the memory hierarchical principles, some researchers have tried to minimize memory system bottlenecks through s...

show abstract

GPU accelerated solver for nonlinear reaction–diffusion systems. Application to the electrophysiology problem

Cited by 24 publications

References 35 publications

Computational model of gastric motility with active‐strain electromechanics

Computational model of gastric motility with active‐strain electromechanics

An atlas‐ and data‐driven approach to initializing reaction‐diffusion systems in computer cardiac electrophysiology

Accelerating simulations of cardiac electrical dynamics through a multi‐GPU platform and an optimized data structure

Contact Info

Product

Resources

About