  Meeting both scalability and performance portability requirements is a
challenge for any HPC application, especially for adaptively refined ones. In
Octo-Tiger, an astrophysics application for the simulation of stellar mergers,
we approach this with existing solutions: We employ HPX to obtain fine-grained
tasks to easily distribute work and finely overlap communication and
computation. For the computations themselves, we use Kokkos to turn these tasks
into compute kernels capable of running on hardware ranging from a few CPU
cores to powerful accelerators. There is a missing link, however: while the
fine-grained parallelism exposed by HPX is useful for scalability, it can
hinder GPU performance when the tasks become too small to saturate the device,
causing low resource utilization. To bridge this gap, we investigate multiple
different GPU work aggregation strategies within Octo-Tiger, adding one new
strategy, and evaluate the node-level performance impact on recent AMD and
NVIDIA GPUs, achieving noticeable speedups.
