  Performance models are well-known instruments to understand the scaling
behavior of parallel applications. They express how performance changes as key
execution parameters, such as the number of processes or the size of the input
problem, vary. Besides reasoning about program behavior, such models can also
be automatically derived from performance data. This is called empirical
performance modeling. While this sounds simple at the first glance, this
approach faces several serious interrelated challenges, including expensive
performance measurements, inaccuracies inflicted by noisy benchmark data, and
overall complex experiment design, starting with the selection of the right
parameters. The more parameters one considers, the more experiments are needed
and the stronger the impact of noise. In this paper, we show how taint
analysis, a technique borrowed from the domain of computer security, can
substantially improve the modeling process, lowering its cost, improving model
quality, and help validate performance models and experimental setups.
