Toward real-time modeling of human heart ventricles at cellular resolution: Simulation of drug-induced arrhythmias

Mirin, A.A.; Richards, David F.; Glosli, James N.; Draeger, Erik W.; Chan, Betty; Fattebert, Jean‐Luc; Krauss, William D.; Oppelstrup, Tomas; Rice, John Jeremy; Gunnels, John A.; Gurev, Viatcheslav; Kim, Changhoan; Magerlein, J. H.; Reumann, Matthias; Wen, Hui-Fang

doi:10.1109/sc.2012.108

Cited by 9 publications

(9 citation statements)

References 23 publications

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“…[24][25][26][27] However, supercomputers are expensive and have high maintenance requirements, and they may not be readily available to a broad group of users. [24][25][26][27] However, supercomputers are expensive and have high maintenance requirements, and they may not be readily available to a broad group of users.…”

Section: Related Workmentioning

confidence: 99%

“…Much effort has been devoted to accelerating computer simulations of cardiac electrophysiology towards real time, many of them requiring high-performance supercomputers. [24][25][26][27] However, supercomputers are expensive and have high maintenance requirements, and they may not be readily available to a broad group of users. For this reason, many researchers are turning to GPUs.…”

Section: Related Workmentioning

confidence: 99%

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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

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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...

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Section: Related Workmentioning

confidence: 99%

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

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“…A related work [22] uses GPU to simulate the complex boundary CFD which is the same type as our work, but this work is conducted on only one GPU. In addition, muscle based cardiac simulation is also a popular research topic [23][24] [25] [26].…”

Section: A Motivationmentioning

confidence: 99%

GPU-accelerated scalable solver for banded linear systems

Liu

Seo

Mittal

et al. 2013

2013 IEEE International Conference on Cluster Computing (CLUSTER)

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Solving a banded linear system efficiently is im portant to many scientific and engineering applications. Current solvers achieve good scalability only on the linear systems that can be partitioned into independent subsystems. In this paper, we present a GPU based, scalable Bi-Conj ugate Gradient Stabilized solver that can be used to solve a wide range of banded linear systems. We utilize a row-oriented matrix decomposition method to divide the banded linear system into several correlated sub linear systems and solve them on multiple GPUs collaboratively. We design a number of GPU and MPI optimizations to speedup inter-GPU and inter-machine communications. We evaluate the solver on Poisson equation and advection diffusion equation as well as several other banded linear systems. The solver achieves a speedup of more than 21 times running from 6 to 192 GPUs on the XSEDE's Keeneland supercomputer and because of small communication overhead, can scale upto 32 GPUs on Amazon EC2 with relatively slow ethernet network.

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“…For the latest simulation strategy based on the EMI (extracellular-membrane-intracellular) approach, see e.g., Tveito et al (2017) ; Jæger et al (2021a) , a high spatial resolution is still needed to resolve the cell membrane surfaces, resulting in considerable computational effort needed to solve the individual ODE systems. Multiple studies have investigated how simulations using the monodomain model or the bidomain model can be scaled to thousands of compute nodes [see e.g., Niederer et al (2011) ; Mirin et al (2012) ; Colli Franzone et al (2018) ]. Operator splitting is typically used with the monodomain, bidomain, and EMI models such that the non-linear ODE part is decoupled from the linear PDE part [see Clayton et al (2011) ; Tveito et al (2017) ].…”

Section: Introductionmentioning

confidence: 99%

Resource-Efficient Use of Modern Processor Architectures For Numerically Solving Cardiac Ionic Cell Models

Hustad

Cai

2022

Front. Physiol.

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A central component in simulating cardiac electrophysiology is the numerical solution of nonlinear ordinary differential equations, also called cardiac ionic cell models, that describe cross-cell-membrane ion transport. Biophysically detailed cell models often require a considerable amount of computation, including calls to special mathematical functions. This paper systematically studies how to efficiently use modern multicore CPUs for this costly computational task. We start by investigating the code restructurings needed to effectively enable compiler-supported SIMD vectorisation, which is the most important performance booster in this context. It is found that suitable OpenMP directives are sufficient for achieving both vectorisation and parallelisation. We then continue with an evaluation of the performance optimisation technique of using lookup tables. Due to increased challenges for automated vectorisation, the obtainable benefits of lookup tables are dependent on the hardware platforms chosen. Throughout the study, we report detailed time measurements obtained on Intel Xeon, Xeon Phi, AMD Epyc and two ARM processors including Fujitsu A64FX, while attention is also paid to the impact of SIMD vectorisation and lookup tables on the computational accuracy. As a realistic example, the benefits of performance enhancement are demonstrated by a 109-run ensemble on the Oakforest-PACS system, where code restructurings and SIMD vectorisation yield an 84% reduction in computing time, corresponding to 63,270 node hours.

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Toward real-time modeling of human heart ventricles at cellular resolution: Simulation of drug-induced arrhythmias

Cited by 9 publications

References 23 publications

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

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

GPU-accelerated scalable solver for banded linear systems

Resource-Efficient Use of Modern Processor Architectures For Numerically Solving Cardiac Ionic Cell Models

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