  In this paper, we demonstrate how GPU-accelerated BEM routines can be used in
a simple black-box fashion to accelerate fast boundary element formulations
based on Hierarchical Matrices (H-Matrices) with ACA (Adaptive Cross
Approximation). In particular, we focus on the expensive evaluation of the
discrete weak form of boundary operators associated with the Laplace and the
Helmholtz equation in three space dimensions. The method is based on offloading
the CPU assembly of elements during the ACA assembly onto a GPU device and to
use threading strategies across ACA blocks to create sufficient workload for
the GPU. The proposed GPU strategy is designed such that it can be implemented
in existing code with minimal changes to the surrounding application structure.
This is in particular interesting for existing legacy code that is not from the
ground-up designed with GPU computing in mind. Our benchmark study gives
realistic impressions of the benefits of GPU-accelerated BEM simulations by
using state-of-the-art multi-threaded computations on modern high-performance
CPUs as a reference, rather than drawing synthetic comparisons with
single-threaded codes. Speed-up plots illustrate that performance gains up to a
factor of 5.5 could be realized with GPU computing under these conditions. This
refers to a boundary element model with about 4 million unknowns, whose
H-Matrix weak form associated with a real-valued (Laplace) boundary operator is
set up in only 100 minutes harnessing the two GPUs instead of 9 hours when
using the 20 CPU cores at disposal only. The benchmark study is followed by a
particularly demanding real-life application, where we compute the scattered
high-frequency sound field of a submarine to demonstrate the increase in
overall application performance from moving to a GPU-based ACA assembly.
