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Summation-by-parts (SBP) finite difference methods are widely used in scientific applications alongside a special treatment of boundary conditions through the simultaneous-approximate-term (SAT) technique which enables the valuable proof of numerical stability. Our work is motivated by multi-scale earthquake cycle simulations described by partial differential equations (PDEs) whose discretizations lead to huge systems of equations and often rely on iterative schemes and parallel implementations to make the nu- merical solutions tractable. In this study, we consider 2D, variable coefficient elliptic PDEs in complex geometries discretized with the SBP-SAT method. The multigrid method is a well-known, efficient solver or preconditioner for traditional numerical discretizations, but they have not been well-developed for SBP-SAT methods on HPC platforms. We propose a custom geometric-multigrid pre- conditioned conjugate-gradient (MGCG) method that applies SBP- preserving interpolations. We then present novel, matrix-free GPU kernels designed specifically for SBP operators whose differences from traditional methods make this task nontrivial but that perform 3× faster than SpMV while requiring only a fraction of memory. The matrix-free GPU implementation of our MGCG method per- forms 5× faster than the SpMV counterpart for the largest problems considered (67 million degrees of freedom). When compared to off- the-shelf solvers in the state-of-the-art libraries PETSc and AmgX, our implementation achieves superior performance in both itera- tions and overall runtime. The method presented in this work offers an attractive solver for simulations using the SBP-SAT method.