Multigrid methods have long been used as efficient solvers for a large class of discrete differential equations. Recently, parallel multigrid solvers have been primarily implemented within an MPI model, with scalability to thousands of cores. With manycore GPU architectures gaining popularity, there is an increased desire to extend multigrid solvers to these architectures. In these environments, algebraic multigrid methods can be cumbersome due to their reliance on indirect addressing. In contrast, structured-grid methods, such as BoxMG, achieve efficiency for heterogeneous problems with only direct addressing. In this talk, I will discuss the advantages of structured-grid multigrid methods over algebraic methods on GPU architectures. I will also present preliminary results of accelerating parallel BoxMG with GPUs.