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Acceleration of cardiac tissue simulation with graphic processing units

In this technical note we show the promise of using graphic processing units (GPUs) to accelerate simulations of electrical wave propagation in cardiac tissue, one of the more demanding computational problems in cardiology. We have found that the computational speed of two-dimensional (2D) tissue si...

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Detalles Bibliográficos
Autores principales: Sato, Daisuke, Xie, Yuanfang, Weiss, James N., Qu, Zhilin, Garfinkel, Alan, Sanderson, Allen R.
Formato: Texto
Lenguaje:English
Publicado: Springer-Verlag 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2734265/
https://www.ncbi.nlm.nih.gov/pubmed/19655187
http://dx.doi.org/10.1007/s11517-009-0514-4
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author Sato, Daisuke
Xie, Yuanfang
Weiss, James N.
Qu, Zhilin
Garfinkel, Alan
Sanderson, Allen R.
author_facet Sato, Daisuke
Xie, Yuanfang
Weiss, James N.
Qu, Zhilin
Garfinkel, Alan
Sanderson, Allen R.
author_sort Sato, Daisuke
collection PubMed
description In this technical note we show the promise of using graphic processing units (GPUs) to accelerate simulations of electrical wave propagation in cardiac tissue, one of the more demanding computational problems in cardiology. We have found that the computational speed of two-dimensional (2D) tissue simulations with a single commercially available GPU is about 30 times faster than with a single 2.0 GHz Advanced Micro Devices (AMD) Opteron processor. We have also simulated wave conduction in the three-dimensional (3D) anatomic heart with GPUs where we found the computational speed with a single GPU is 1.6 times slower than with a 32-central processing unit (CPU) Opteron cluster. However, a cluster with two or four GPUs is faster than the CPU-based cluster. These results demonstrate that a commodity personal computer is able to perform a whole heart simulation of electrical wave conduction within times that enable the investigators to interact more easily with their simulations.
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spelling pubmed-27342652009-09-02 Acceleration of cardiac tissue simulation with graphic processing units Sato, Daisuke Xie, Yuanfang Weiss, James N. Qu, Zhilin Garfinkel, Alan Sanderson, Allen R. Med Biol Eng Comput Technical Note In this technical note we show the promise of using graphic processing units (GPUs) to accelerate simulations of electrical wave propagation in cardiac tissue, one of the more demanding computational problems in cardiology. We have found that the computational speed of two-dimensional (2D) tissue simulations with a single commercially available GPU is about 30 times faster than with a single 2.0 GHz Advanced Micro Devices (AMD) Opteron processor. We have also simulated wave conduction in the three-dimensional (3D) anatomic heart with GPUs where we found the computational speed with a single GPU is 1.6 times slower than with a 32-central processing unit (CPU) Opteron cluster. However, a cluster with two or four GPUs is faster than the CPU-based cluster. These results demonstrate that a commodity personal computer is able to perform a whole heart simulation of electrical wave conduction within times that enable the investigators to interact more easily with their simulations. Springer-Verlag 2009-08-05 2009-09 /pmc/articles/PMC2734265/ /pubmed/19655187 http://dx.doi.org/10.1007/s11517-009-0514-4 Text en © The Author(s) 2009
spellingShingle Technical Note
Sato, Daisuke
Xie, Yuanfang
Weiss, James N.
Qu, Zhilin
Garfinkel, Alan
Sanderson, Allen R.
Acceleration of cardiac tissue simulation with graphic processing units
title Acceleration of cardiac tissue simulation with graphic processing units
title_full Acceleration of cardiac tissue simulation with graphic processing units
title_fullStr Acceleration of cardiac tissue simulation with graphic processing units
title_full_unstemmed Acceleration of cardiac tissue simulation with graphic processing units
title_short Acceleration of cardiac tissue simulation with graphic processing units
title_sort acceleration of cardiac tissue simulation with graphic processing units
topic Technical Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2734265/
https://www.ncbi.nlm.nih.gov/pubmed/19655187
http://dx.doi.org/10.1007/s11517-009-0514-4
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