  We propose a concise approximate description, and a method for efficiently
obtaining this description, via adaptive random sampling of the performance
(running time, memory consumption, or any other profileable numerical quantity)
of a given algorithm on some low-dimensional rectangular grid of inputs. The
formal correctness is proven under reasonable assumptions on the algorithm
under consideration; and the approach's practical benefit is demonstrated by
predicting for which observer positions and viewing directions an occlusion
culling algorithm yields a net performance benefit or loss compared to a simple
brute force renderer.
