Performance Engineering (PE) is a structured, model-based process for the structured optimization and parallelization of basic operations, algorithms and application codes for modern compute architectures. The process is divided into analysis, modeling and optimization phases, which are iterated for each homogeneous code section until an optimal or satisfactory performance is achieved. During the analysis, the first step is to develop a hypothesis about which aspect of the architecture (bottleneck) limits the execution speed of the software. The qualitative identification of typical bottlenecks can be done with so-called application-independent performance patterns. A concrete performance pattern is described by a set of observable runtime characteristics. Using suitable performance models, the interaction of the application with the given hardware architecture is then described analytically and quantitatively.
The model thus indicates the maximum expected performance and potential runtime improvements through appropriate modifications. If the model predictions cannot be validated by measurements, the underlying model assumptions are revisited and refined or adjusted if necessary. Based on the model, optimizations can be planned and their performance gain be assessed a-priori. The PE approach is not limited to standard microprocessor architectures and can also be used for projections to future computer architectures. The main focus of the group is on the computational node, where analytic performance models such as the Roofline model or the Execution Cache Memory (ECM) model are used.