Limits...
Acceleration of cardiac tissue simulation with graphic processing units.

Sato D, Xie Y, Weiss JN, Qu Z, Garfinkel A, Sanderson AR - Med Biol Eng Comput (2009)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Cardiovascular Research Laboratory, Department of Medicine (Cardiology), David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. dasato@mednet.ucla.edu

ABSTRACT
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.

Show MeSH
Sample GPU code to solve the reaction-diffusion equation in 2D tissue. At each time step, the ODE part is called once and the PDE part is called four times. The ODE kernel code and the PDE kernel code are in the Appendix
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2734265&req=5

Fig1: Sample GPU code to solve the reaction-diffusion equation in 2D tissue. At each time step, the ODE part is called once and the PDE part is called four times. The ODE kernel code and the PDE kernel code are in the Appendix

Mentions: The cardiac tissue was modeled using the following partial differential equation:\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$ {\frac{\partial V}{\partial t}} = - {\frac{I}{{C_{m} }}} + \nabla \cdot D\nabla V, $$\end{document}where V is the transmembrane voltage, I is the total ionic current, Cm is the transmembrane capacitance, and D is the diffusion tensor. The cell model used in this study was phase I of the Luo–Rudy action potential model [3]. We solved this reaction-diffusion equation with the forward Euler method, using the technique of operator splitting [6]. The time step was adaptively varied between 0.01 and 0.1 ms and the space step was 0.015 cm. Details of the modeling of cardiac tissue are described in our previous study [10]. For each time step, the ODE part was solved once and the PDE part was solved four times for the 2D simulation and six times for the 3D simulation (Fig. 1).Fig. 1


Acceleration of cardiac tissue simulation with graphic processing units.

Sato D, Xie Y, Weiss JN, Qu Z, Garfinkel A, Sanderson AR - Med Biol Eng Comput (2009)

Sample GPU code to solve the reaction-diffusion equation in 2D tissue. At each time step, the ODE part is called once and the PDE part is called four times. The ODE kernel code and the PDE kernel code are in the Appendix
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2734265&req=5

Fig1: Sample GPU code to solve the reaction-diffusion equation in 2D tissue. At each time step, the ODE part is called once and the PDE part is called four times. The ODE kernel code and the PDE kernel code are in the Appendix
Mentions: The cardiac tissue was modeled using the following partial differential equation:\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$ {\frac{\partial V}{\partial t}} = - {\frac{I}{{C_{m} }}} + \nabla \cdot D\nabla V, $$\end{document}where V is the transmembrane voltage, I is the total ionic current, Cm is the transmembrane capacitance, and D is the diffusion tensor. The cell model used in this study was phase I of the Luo–Rudy action potential model [3]. We solved this reaction-diffusion equation with the forward Euler method, using the technique of operator splitting [6]. The time step was adaptively varied between 0.01 and 0.1 ms and the space step was 0.015 cm. Details of the modeling of cardiac tissue are described in our previous study [10]. For each time step, the ODE part was solved once and the PDE part was solved four times for the 2D simulation and six times for the 3D simulation (Fig. 1).Fig. 1

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Cardiovascular Research Laboratory, Department of Medicine (Cardiology), David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. dasato@mednet.ucla.edu

ABSTRACT
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.

Show MeSH