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We present GAMER-2, a GPU-accelerated adaptive mesh refinement (AMR) code forastrophysics. It provides a rich set of features, including adaptivetime-stepping, several hydrodynamic schemes, magnetohydrodynamics,self-gravity, particles, star formation, chemistry and radiative processes withGRACKLE, data analysis with yt, and memory pool for efficient objectallocation. GAMER-2 is fully bitwise reproducible. For the performanceoptimization, it adopts hybrid OpenMP/MPI/GPU parallelization and utilizesoverlapping CPU computation, GPU computation, and CPU-GPU communication. Loadbalancing is achieved using a Hilbert space-filling curve on a level-by-levelbasis without the need to duplicate the entire AMR hierarchy on each MPIprocess. To provide convincing demonstrations of the accuracy and performanceof GAMER-2, we directly compare with Enzo on isolated disk galaxy simulationsand with FLASH on galaxy cluster merger simulations. We show that the physicalresults obtained by different codes are in very good agreement, and GAMER-2outperforms Enzo and FLASH by nearly one and two orders of magnitude,respectively, on the Blue Waters supercomputers using $1-256$ nodes. Moreimportantly, GAMER-2 exhibits similar or even better parallel scalabilitycompared to the other two codes. We also demonstrate good weak and strongscaling using up to 4096 GPUs and 65,536 CPU cores, and achieve a uniformresolution as high as $10{,}240^3$ cells. Furthermore, GAMER-2 can be adoptedas an AMR+GPUs framework and has been extensively used for the wave dark matter($\psi$DM) simulations. GAMER-2 is open source (available athttps://github.com/gamer-project/gamer) and new contributions are welcome.

GAMER-2: a GPU-accelerated adaptive mesh refinement code -- accuracy, performance, and scalability