  We present an efficient, robust and fully GPU-accelerated aggregation-based
algebraic multigrid preconditioning technique for the solution of large sparse
linear systems. These linear systems arise from the discretization of elliptic
PDEs. The method involves two stages, setup and solve. In the setup stage,
hierarchical coarse grids are constructed through aggregation of the fine grid
nodes. These aggregations are obtained using a set of maximal independent nodes
from the fine grid nodes. We use a ``fine-grain'' parallel algorithm for
finding a maximal independent set from a graph of strong negative connections.
The aggregations are combined with a piece-wise constant (unsmooth)
interpolation from the coarse grid solution to the fine grid solution, ensuring
low setup and interpolation cost. The grid independent convergence is achieved
by using recursive Krylov iterations (K-cycles) in the solve stage. An
efficient combination of K-cycles and standard multigrid V-cycles is used as
the preconditioner for Krylov iterative solvers such as generalized minimal
residual and conjugate gradient. We compare the solver performance with other
solvers based on smooth aggregation and classical algebraic multigrid methods.
