Magnetising the quokka code with fast, second-order accurate, error-correcting schemes for magnetohydrodynamics on GPUs

Abstract

We implement a second-order accurate constrained transport (CT) magnetohydrodynamics (MHD) module with first-order flux correction in the open-source, adaptive mesh refinement (AMR), GPU-accelerated code quokka, supporting both ideal and resistive (constant Ohmic) regimes. For computing and averaging the electromotive forces (EMFs) at cell edges, the basis of any CT-MHD method, we experiment with a wide range of recent, state-of-the-art schemes, together with different reconstruction schemes. Alongside these, we develop a new EMF compute scheme that requires fewer reconstruction steps than existing approaches. We evaluate these scheme combinations on the basis of accuracy, stability, and GPU throughput, and demonstrate that our new scheme achieves accuracy comparable to the best-in-class existing methods, together with greater stability in reconnection-dominated flows and roughly 25--60% higher GPU throughput. Using this scheme, quokka achieves excellent results across a wide range of MHD flow regimes, reaching>50 million cell updates per GPU per second, with>70% parallel efficiency out to>500 GPUs. Finally, we confirm that our CT implementation preserves divergence-free magnetic fields (to machine precision) under AMR.