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Enhancing scalability of parallel structured AMR calculations

We discuss parallel performance of structured adaptive mesh refinement calculations using the SAMRAI library. We focus on fundamental aspects of adaptive gridding and dynamic computation of changing data dependencies. Previous analysis of performance of large-scale parallel adaptive calculations revealed poor scaling in these operations. Specifically, we found that these operations are inexpensive for small problems, but that their costs can become unacceptable for problems run on large numbers of processors. This paper describes subsequent developments involving graph- and tree-based algorithms that reduce runtime complexity and substantially increase scalability. We characterize performance on realistic adaptive problems using up to 512 processors of an IBM SP system and up to 1024 processors of a Linux cluster.