Model-guided performance tuning of parameter values: A case study with molecular dynamics visualization

In this paper, we consider the interaction between application programmers and tools that automatically search a space of application-level parameters that are believed to impact the performance of an application significantly. We study performance tuning of a large scientific application, the visualization component of a molecular dynamics simulation. The key contribution of the approach is the use of high-level programmer-specified models of the expected performance behavior of individual parameters. We use these models to reduce the search space associated with the range of parameter values and achieve results that perform close to that of a more exhaustive search of the parameter space. With this case study, we show the importance of appropriate parameter selection, with the difference between best-case and worst-case performance with a particular input data set and processor configuration of up to a factor of 17. We show that through the use of models, we can drastically reduce search time, examining only 0.3% to 5% of the search space, and usually select an implementation that is close to the best performance, within 0.84% to 15%, even though the models are not completely accurate.