  Development of highly scalable and robust algorithms for large-scale CFD
simulations has been identified as one of the key ingredients to achieve NASA's
CFD Vision 2030 goals. In order to improve simulation capability and to
effectively leverage new high-performance computing hardware, the most
computationally intensive parts of CFD solution algorithms -- namely, linear
solvers and preconditioners -- need to achieve asymptotic behavior on massively
parallel and heterogeneous architectures and preserve convergence rates as the
meshes are refined further. In this work, we present a scalable high-order
implicit Discontinuous Galerkin solver from the SU2 framework using a promising
preconditioning technique based on algebraic sparsified nested dissection
algorithm with low-rank approximations, and communication-avoiding Krylov
subspace methods to enable scalability with very large processor counts. The
overall approach is tested on a canonical 2D NACA0012 test case of increasing
size to demonstrate its scalability on multiple processing cores. Both the
preconditioner and the linear solver are shown to exhibit near-linear weak
scaling up to 2,048 cores with no significant degradation of the convergence
rate.
