  Incomplete LU (ILU) smoothers are effective in the algebraic multigrid (AMG)
$V$-cycle for reducing high-frequency components of the error. However, the
requisite direct triangular solves are comparatively slow on GPUs. Previous
work has demonstrated the advantages of Jacobi iteration as an alternative to
direct solution of these systems. Depending on the threshold and fill-level
parameters chosen, the factors can be highly non-normal and Jacobi is unlikely
to converge in a low number of iterations. We demonstrate that row scaling can
reduce the departure from normality, allowing us to replace the inherently
sequential solve with a rapidly converging Richardson iteration. There are
several advantages beyond the lower compute time. Scaling is performed locally
for a diagonal block of the global matrix because it is applied directly to the
factor. Further, an ILUT Schur complement smoother maintains a constant GMRES
iteration count as the number of MPI ranks increases, and thus parallel
strong-scaling is improved. Our algorithms have been incorporated into hypre,
and we demonstrate improved time to solution for linear systems arising in the
Nalu-Wind and PeleLM pressure solvers. For large problem sizes, GMRES$+$AMG
executes at least five times faster when using iterative triangular solves
compared with direct solves on massively-parallel GPUs.
