Fully GPU-accelerated, matrix-free immersed boundary method for complex fiber-reinforced hyperelastic cardiac models

Abstract

The immersed boundary (IB) method has become a leading approach in cardiac fluid-structure interaction (FSI) modeling due to its ability to handle large deformations and complex geometries without requiring mesh regeneration. However, the use of nonlinear, fiber-reinforced hyperelastic materials for modeling soft cardiac tissues introduces challenges in computational efficiency, particularly due to the additional projection steps required for stability in the IB framework. These steps often involve sparse matrix storage and computation, which can degrade GPU performance. In this work, we present a fully GPU-accelerated, matrix-free IB method for FSI in anatomically realistic cardiac models, which novelly integrates established components into a unified, GPU-optimized system. By employing nodal coupling, our method eliminates the need for projection operations in the finite element space. Additionally, we solve the Navier-Stokes equations using Chorin’s projection method combined with a matrix-free geometric multigrid solver, ensuring the entire FSI algorithm remains matrix-free and highly compatible with GPU acceleration. Our implementation features several GPU-specific optimizations, including the use of constant memory to store values of nodal basis functions and their derivatives at quadrature points, and texture memory to efficiently implement the semi-Lagrangian discretization of convection terms. These innovations maximize GPU utilization while preserving the complex mechanical behavior of soft cardiac tissue. Benchmark tests demonstrate that our GPU-accelerated solver achieves a $50\times -100\times$ speedup compared to a 20-core CPU implementation, with comparable accuracy. Critically, this performance enables clinically viable cardiac valve FSI simulations to be completed within a few hours on a single consumer-grade GPU-an achievement that was previously infeasible using traditional CPU-based frameworks.