  Traditional static resource analyses estimate the total resource usage of a
program, without executing it. In this paper we present a novel resource
analysis whose aim is instead the static profiling of accumulated cost, i.e.,
to discover, for selected parts of the program, an estimate or bound of the
resource usage accumulated in each of those parts. Traditional resource
analyses are parametric in the sense that the results can be functions on input
data sizes. Our static profiling is also parametric, i.e., our accumulated cost
estimates are also parameterized by input data sizes. Our proposal is based on
the concept of cost centers and a program transformation that allows the static
inference of functions that return bounds on these accumulated costs depending
on input data sizes, for each cost center of interest. Such information is much
more useful to the software developer than the traditional resource usage
functions, as it allows identifying the parts of a program that should be
optimized, because of their greater impact on the total cost of program
executions. We also report on our implementation of the proposed technique
using the CiaoPP program analysis framework, and provide some experimental
results. This paper is under consideration for acceptance in TPLP.
