Visualization of Scalar Adaptive Mesh Refinement Data

Adaptive Mesh Refinement (AMR) is a highly effective computation method for simulations that span a large range of spatiotemporal scales, such as astrophysical simulations, which must accommodate ranges from interstellar to sub-planetary. Most mainstream visualization tools still lack support for AMR grids as a first class data type and AMR code teams use custom built applications for AMR visualization. The Department of Energy's (DOE's) Science Discovery through Advanced Computing (SciDAC) Visualization and Analytics Center for Enabling Technologies (VACET) is currently working on extending VisIt, which is an open source visualization tool that accommodates AMR as a first-class data type. These efforts will bridge the gap between general-purpose visualization applications and highly specialized AMR visual analysis applications. Here, we give an overview of the state of the art in AMR scalar data visualization research

Small Scale Processes and Entrainment in a Stratocumulus Marine Boundary Layer

Numerical studies of boundary layer meteorology are increasingly relying on large eddy simulation (LES) models, but few detailed validation studies of these types of models have been done. In this paper we investigate the behavior of an LES model for simulation of a marine boundary layer. Specifically, we focus on the mechanisms which control numerical predictions of entrainment into the tops of marine stratus in a moist generalization of the 1995 GCSS (GEWEX Cloud System Studies) model intercomparison. For the computational study we present a sequence of simulations of varying resolution, from a typical resolution (50m horizontally and 25m vertically) to a fine resolution (8m horizontally and 4m vertically). We also explore variations in the model such as different subgrid models and modifications of the advection scheme. We do not expect to observe traditional numerical convergence of the solutions under grid refinement; instead we look at various statistical measures of the solution..

Performance of a Block Structured, Hierarchical Adaptive MeshRefinement Code on the 64k Node IBM BlueGene/L Computer

We describe the performance of the block-structured Adaptive Mesh Refinement (AMR) code Raptor on the 32k node IBM BlueGene/L computer. This machine represents a significant step forward towards petascale computing. As such, it presents Raptor with many challenges for utilizing the hardware efficiently. In terms of performance, Raptor shows excellent weak and strong scaling when running in single level mode (no adaptivity). Hardware performance monitors show Raptor achieves an aggregate performance of 3:0 Tflops in the main integration kernel on the 32k system. Results from preliminary AMR runs on a prototype astrophysical problem demonstrate the efficiency of the current software when running at large scale. The BG/L system is enabling a physics problem to be considered that represents a factor of 64 increase in overall size compared to the largest ones of this type computed to date. Finally, we provide a description of the development work currently underway to address our inefficiencies

AMReX-Codes/amrex: AMReX 23.11

AMReX: Software Framework for Block Structured AM