  The reliability of cardiovascular computational models depends on the
accurate solution of the hemodynamics, the realistic characterization of the
hyperelastic and electric properties of the tissues along with the correct
description of their interaction. The resulting
fluid-structure-electrophysiology interaction (FSEI) thus requires an immense
computational power, usually available in large supercomputing centers, and
requires long time to obtain results even if multi-CPU processors are used (MPI
acceleration). In recent years, graphics processing units (GPUs) have emerged
as a convenient platform for high performance computing, as they allow for
considerable reductions of the time-to-solution. This approach is particularly
appealing if the tool has to support medical decisions that require solutions
within reduced times and possibly obtained by local computational resources.
Accordingly, our multi-physics solver has been ported to GPU architectures
using CUDA Fortran to tackle fast and accurate hemodynamics simulations of the
human heart without resorting to large-scale supercomputers. This work
describes the use of CUDA to accelerate the FSEI on heterogeneous clusters,
where both the CPUs and GPUs are used in synergistically with minor
modifications of the original source code. The resulting GPU accelerated code
solves a single heartbeat within a few hours (from three to ten depending on
the grid resolution) running on premises computing facility made of few GPU
cards, which can be easily installed in a medical laboratory or in a hospital,
thus opening towards a systematic computational fluid dynamics (CFD) aided
diagnostic.
