Off-Road Performance Modeling – How to Deal with Segmented Data

Besides correctness, scalability is one of the top priorities of parallel programmers. With manual analytical performance modeling often being too laborious, developers increasingly resort to empirical performance modeling as a viable alternative, which learns performance models from a limited amount of performance measurements. Although powerful automatic techniques exist for this purpose, they usually struggle with the situation where performance data representing two or more different phenomena are conflated into a single performance model. This not only generates an inaccurate model for the given data, but can also either fail to point out existing scalability issues or create the appearance of such issues when none are present. In this paper, we present an algorithm to detect segmentation in a sequence of performance measurements and estimate the point where the behavior changes. Our method correctly identified segmentation in more than 80% of 5.2 million synthetic tests and confirmed expected segmentation in three application case studies.