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GPU Accelerated Cardiac Arrhythmia Simulations for Enhanced Efficacy

Explore speeding up cardiac arrhythmia simulations using GPGPU for improved treatment outcomes. Learn about the CUDA command, GPU acceleration considerations, and simulation results. Contact Wei Wang at weiwan@udel.edu for details. Collaboration by Wei Wang, H. Howie Huang, Matthew Kay, and John Cavazos from the University of Delaware and The George Washington University.

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GPU Accelerated Cardiac Arrhythmia Simulations for Enhanced Efficacy

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  1. GPGPU Accelerated Cardiac Arrhythmia Simulations Wei Wang1, H. HowieHuang2, Matthew Kay2 and John Cavazos1 University of Delaware The George Washington University

  2. Motivation • Cardiac arrhythmia • ~300,000 people/year in the US • Cure • Image-guided ablation therapy • Simulation improves efficacy • Problem • Sequential execution of simulation too slow!

  3. Simulation: Cardiac Model

  4. Acceleration Concept N11 N21 N31 Sequential (Running on CPU) Parallel (Enabled by GPGPUs) N12 N22 N32 N13 N23 N33

  5. Acceleration Tool—GPU • Example: NVIDIA Tesla C1060 • 240 Processing Elements • Massively parallel multithreaded • Up to 30720 active threads CPU GPU

  6. Acceleration Considerations • SIMD • Large Matrix • No (Few) Temporal Data Dependency • Acceleration Command—CUDA • Using Atomic Functions*

  7. GPU Acceleration for (Xstep=1;Xstep<Nx+1;++Xstep){ for (Ystep=1;Ystep<Ny+1;++Ystep){ stimulate(); //apply stimulating current brgates(); // update gating equations brcurrents(); // update currents mdiff(); // update diffusion terms } // end Ystep loop } // end Xstep loop bcs(); // apply boundary conditions GPU_ GPU_ GPU_ GPU_

  8. Simulation Results Point Stimulation Electrical Rotor Simulation

  9. Point Simulation Results Speedup (normalized to seq perf) Matrix Sizes

  10. Thank you! Wei Wang weiwang@udel.edu http://www.cis.udel.edu/~wwang Please consider GPGPU

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