PEMOGEN: Automatic Adaptive Performance Modeling during Program Runtime

Traditional means of gathering performance data are trac-ing, which is limited by the available storage, and profiling, which has limited accuracy. Performance modeling is often used to interpret the tracing data and generate performance predictions. We aim to complement the traditional data collection mechanisms with online performance modeling, a method that generates performance models while the appli-cation is running. This allows us to greatly reduce the stor-age overhead while still producing accurate predictions. We present PEMOGEN, our compilation and modeling frame-work that automatically instruments applications to gen-erate performance models during program execution. We demonstrate the ability of PEMOGEN to both reduce stor-age cost and improve the prediction accuracy compared to traditional techniques such as least squares fitting. With our tool, we automatically detect 3,370 kernels from fifteen NAS and Mantevo applications and model their execution time with a median coefficient of variation (R̄2) of 0.81. These automatically generated performance models can be used to quickly assess the scaling and potential bottlenecks with re-gards to any input parameter and the number of processes of a parallel application. 1