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Title of Journal: Med Biol Eng Comput

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Abbravation: Medical & Biological Engineering & Computing

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Springer-Verlag

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DOI

10.1002/ddr.430170201

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1741-0444

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Acceleration of cardiac tissue simulation with gra

Authors: Daisuke Sato Yuanfang Xie James N Weiss Zhilin Qu Alan Garfinkel Allen R Sanderson
Publish Date: 2009/08/05
Volume: 47, Issue: 9, Pages: 1011-1015
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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 twodimensional 2D tissue simulations with a single commercially available GPU is about 30 times faster than with a single 20 GHz Advanced Micro Devices AMD Opteron processor We have also simulated wave conduction in the threedimensional 3D anatomic heart with GPUs where we found the computational speed with a single GPU is 16 times slower than with a 32central processing unit CPU Opteron cluster However a cluster with two or four GPUs is faster than the CPUbased 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 simulationsIn the last few decades computer simulation has become an important tool to investigate various phenomena in cardiac biology including studies of single ion channel properties 9 action potentials of the myocyte 3 5 dynamics of action potential propagation in tissue 2 subcellular calcium dynamics 7 etc In spite of the advancement of computational technology the simulation of action potential waves in threedimensional 3D cardiac tissue with a realistic geometry is still considered as a “largescale simulation”Generalpurpose computing on GPUs GPGPU is a recently emerging technology 1 4 8 which uses GPUs instead of CPUs to compute large simulations in parallel GPUs are massively parallel single instruction multiple data processing units Each GPU may contain 128–240 “stream processors” whereas today’s CPUs contain 2 4 or 8 cores In this paper we demonstrate that the GPU is about 30~40 times faster than the CPU enabling it to perform whole heart electrophysiology simulations within practical timeIn this study we chose the simulation of the propagation of the action potential in cardiac tissue which is modeled as the propagation of a wave in an excitable medium Therefore this technique can be applied to a number of phenomena in physics chemistry and biologyWe used two test models The first was a 2D homogeneous sheet and the second was an anatomic rabbit ventricular model with ‘fiber rotation’ 10 that is an anisotropy that varies from point to point in the heart Each model was simulated using both the GPUs and CPUsThe GPU simulation was performed with a single NVIDIA Geforce 8800 GT 1GB Graphic randomaccess memory RAM and an NVIDIA Geforce 9800 GX2 1GB Graphic RAM These graphic cards were installed into a system with a dualcore 20 GHz AMD Opteron processor and 4GB error correction code ECC RAM The operating system is OpenSUSE 102 Our programs are written in C++ We used GNU C++ compiler version 412 and NVIDIA CUDA version 11The CPU simulation was performed with an 8node high performancecomputing HPC cluster Each node has two dualcore 20 GHz AMD Opteron processors ie 4 cores in each node and 4GB ECC RAM The operating system is Fedora Core 5 We used an Intel C++ compiler 101 In order to parallelize on this cluster we used Message Passing Interface 10 The FORTRAN version of this code was used in some of our previous studies 10


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