Scaling Properties of Common Statistical Operators for Gridded Datasets

An accurate cost-model that accounts for dataset size and structure can help optimize geoscience data analysis. We develop and apply a computational model to estimate data analysis costs for arithmetic operations on gridded datasets typical of satelliteor climate model-origin. For these dataset geometries our model predicts data reduction scalings that agree with measurements of widely-used geoscience data processing software, the netCDF Operators (NCO). I/O performance and library design dominate throughput for simple analysis (e.g., dataset differencing). Dataset structure can reduce analysis throughput ten-fold relative to same-sized unstructured datasets. We demonstrate algorithmic optimizations which substantially increase throughput for more complex, arithmetic-dominated analysis such as weighted-averaging of multi-dimensional data. These scaling properties can help to estimate costs of distribution strategies for data reduction in cluster and grid environments.