On the usefulness of object tracking techniques in performance analysis

Understanding the behavior of a parallel application is crucial if we are to tune it to achieve its maximum performance. Yet the behavior the application exhibits may change over time and depend on the actual execution scenario: particular inputs and program settings, the number of processes used, or hardware-specific problems. So beyond the details of a single experiment a far more interesting question arises: how does the application behavior respond to changes in the execution conditions? In this paper, we demonstrate that object tracking concepts from computer vision have huge potential to be applied in the context of performance analysis. We leverage tracking techniques to analyze how the behavior of a parallel application evolves through multiple scenarios where the execution conditions change. This method provides comprehensible insights on the influence of different parameters on the application behavior, enabling us to identify the most relevant code regions and their performance trends.