  This report summarizes the weak scaling performance of the
diffusion-synthetic acceleration (DSA) of transport sweeps in PDT (a massively
parallel deterministic transport solver developed at Texas A\&M University). It
provides an assessment of the cost of the DSA based on the modified symmetric
interior-penalty discontinuous Galerkin discretization and solved by the
AMG-preconditioned conjugate gradient method provided by the HYPRE/BoomerAMG
library, relative to the transport sweeps. Different parallel partitionings
(``hybrid-KBA'', volumetric), per-core workload (512/4k cells per core) and
number of discrete directions (128 to 2k) are compared and a set of optimized
HYPRE/BoomerAMG parameters is selected that leads to best overall performance
of the transport+DSA solution at scale.
