  Multigrid solvers are the standard in modern scientific computing
simulations. Domain Decomposition Aggregation-Based Algebraic Multigrid, also
known as the DD-$\alpha$AMG solver, is a successful realization of an algebraic
multigrid solver for lattice quantum chromodynamics. Its CPU implementation has
made it possible to construct, for some particular discretizations, simulations
otherwise computationally unfeasible, and furthermore it has motivated the
development and improvement of other algebraic multigrid solvers in the area.
From an existing version of DD-$\alpha$AMG already partially ported via CUDA to
run some finest-level operations of the multigrid solver on Nvidia GPUs, we
translate the CUDA code here by using HIP to run on the ORISE supercomputer. We
moreover extend the smoothers available in DD-$\alpha$AMG, paying particular
attention to Richardson smoothing, which in our numerical experiments has led
to a multigrid solver faster than smoothing with GCR and only 10% slower
compared to SAP smoothing. Then we port the odd-even-preconditioned versions of
GMRES and Richardson via CUDA. Finally, we extend some computationally
intensive coarse-grid operations via advanced vectorization.
