Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Computing Performance Comparison of CPU and GPU Parallelization for Virtual Heart Simulation

가상 심장 시뮬레이션에서 CPU와 GPU 병렬처리의 계산 성능 비교

  • Kim, Sang Hee (Dept of IT Convergence Engineering, Kumoh National Institute of Technology) ;
  • Jeong, Da Un (Dept of Medical IT Convergence Engineering, Kumoh National Institute of Technology) ;
  • Setianto, Febrian (Dept of IT Convergence Engineering, Kumoh National Institute of Technology) ;
  • Lim, Ki Moo (Dept of IT Convergence Engineering, Kumoh National Institute of Technology)
  • 김상희 (금오공과대학교 IT융복합공학과) ;
  • 정다운 (금오공과대학교 메디컬IT융합공학과) ;
  • ;
  • 임기무 (금오공과대학교 IT융복합공학과)
  • Received : 2020.05.21
  • Accepted : 2020.06.11
  • Published : 2020.06.30
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Abstract

Cardiac electrophysiology studies often use simulation to predict how cardiac will behave under various conditions. To observe the cardiac tissue movement, it needs to use the high--resolution heart mesh with a sophisticated and large number of nodes. The higher resolution mesh is, the more computation time is needed. To improve computation speed and performance, parallel processing using multi-core processes and network computing resources is performed. In this study, we compared the computational speeds of CPU parallelization and GPU parallelization in virtual heart simulation for efficiently calculating a series of ordinary differential equations (ODE) and partial differential equations (PDE) and determined the optimal CPU and GPU parallelization architecture. We used 2D tissue model and 3D ventricular model to compared the computation performance. Then, we measured the time required to the calculation of ODEs and PDEs, respectively. In conclusion, for the most efficient computation, using GPU parallelization rather than CPU parallelization can improve performance by 4.3 times and 2.3 times in calculations of ODEs and PDE, respectively. In CPU parallelization, it is best to use the number of processors just before the communication cost between each processor is incurred.

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