Performance Characteristics of an Adaptive Mesh RefinementCalculation on Scalar and Vector Platforms

Adaptive mesh refinement (AMR) is a powerful technique thatreduces the resources necessary to solve otherwise in-tractable problemsin computational science. The AMR strategy solves the problem on arelatively coarse grid, and dynamically refines it in regions requiringhigher resolution. However, AMR codes tend to be far more complicatedthan their uniform grid counterparts due to the software infrastructurenecessary to dynamically manage the hierarchical grid framework. Despitethis complexity, it is generally believed that future multi-scaleapplications will increasingly rely on adaptive methods to study problemsat unprecedented scale and resolution. Recently, a new generation ofparallel-vector architectures have become available that promise toachieve extremely high sustained performance for a wide range ofapplications, and are the foundation of many leadership-class computingsystems worldwide. It is therefore imperative to understand the tradeoffsbetween conventional scalar and parallel-vector platforms for solvingAMR-based calculations. In this paper, we examine the HyperCLaw AMRframework to compare and contrast performance on the Cray X1E, IBM Power3and Power5, and SGI Altix. To the best of our knowledge, this is thefirst work that investigates and characterizes the performance of an AMRcalculation on modern parallel-vector systems