New elevation sampling technology provides digital elevation data at unprecedented resolution. Multiscale analytical methods are becoming increasingly important for meaningful analysis as a result of increasingly fine-resolution datasets. However, high computational complexity often limits the feasibility and quality of the analysis. This research sought to investigate and develop metrics and methods for geomorphometric analyses that provide the computational efficiency required of hypercscaled analyses on fine-resolution data. Local topographic position (LTP) metrics are a class of elevation indices that benefit greatly from hyperscale analytical techniques, yet there is little guidance for metric selection in the literature. Four efficiency-optimized algorithms LTP metrics were benchmarked to document their performance. Having established DEV as the optimal metric for hyperscale analysis, it was modified to measure landscape topographic anisotropy using oriented windows. The novel terrain attribute had the sensitivity to detect even complex nested anisotropic features and the efficiency to feasibly sample in hyperscale.