A comparison of multithreading, vectorization, and GPU computing for the acceleration of cardiac electrophysiology models

Chiheb Sakka1, Amina Guermouche2, Olivier Aumage2, Emmanuelle Saillard2, Mark Potse3, Yves Coudière3, Denis Barthou2
1National Institute for Research in Digital Science and Technology, 2Inria Bordeaux Sud-Ouest, 3Université de Bordeaux


Introduction: Realistic simulation of cardiac electrophysiology requires both high resolution and computationally expensive models of membrane dynamics. Optimization of membrane models can therefore have a large impact on time, hardware, and energy usage. We tested both CPU-based and GPU-based optimization techniques for a human heart model with Ten Tusscher-Panfilov 2006 dynamics.

Methods: Our baseline code was parallelized with MPI and OpenMP. Optimization for CPUs with single-instruction multiple-data (SIMD) capabilities was performed using MIPP library functions and an adapted storage order for the membrane status variables. Optimization for GPUs was achieved using multiple CUDA streams to overlap computation time with memory transfers. Only the membrane model was optimized. Comparisons were made on a monodomain model of the human ventricles with 0.1-mm resolution and 80 million model nodes, four Intel CPUs (either Xeon E5-2683 v4 "Broadwell" or Xeon Gold 6240 "Cascade Lake"), and two NVIDIA Tesla P100 GPUs.

Results: Compared to a multithreaded code running on 64 CPU cores, the P100 GPU ran the membrane model about 3 times faster (1.5 times faster for the whole code). GPU performance was bounded by the data transfer rate between GPU and main memory. Effective use of the CPU's SIMD capabilities allowed a similar performance gain. Optimal SIMD use required explicit vectorization and an adapted data structure. The 512-bit AVX512 instruction set did not perform faster than the 256-bit AVX2 instruction set.

Discussion: In our test case both the use of a GPU and effective SIMD usage were 3 times faster than a naive multithreaded code. We conclude that on mixed CPU-GPU systems the best results could be obtained by optimizing both CPU and GPU code while using a runtime system that balances CPU and GPU load empirically.