A new parallelization scheme for adaptive mesh refinement

We present a new method for parallelization of adaptive mesh refinement called Concurrent Structured Adaptive Mesh Refinement (CSAMR). This new method offers the lower computational cost (i.e. wall time × processor count) of subcycling in time, but with the runtime performance (i.e. smaller wall time) of evolving all levels at once using the time step of the finest level (which does more work than subcycling but has less parallelism). We demonstrate our algorithm's effectiveness using an adaptive mesh refinement code, AMSS-NCKU, and show performance on Blue Waters and other high performance clusters. For the class of problem considered in this paper, our algorithm achieves a speedup of 1.7-1.9 when the processor count for a given AMR run is doubled, consistent with our theoretical predictions.