Programmers can no longer depend on new processors to have significantly improved single-thread performance. Instead, gains have to come from other sources such as the compiler and its optimization passes. Advanced passes make use of information on the dependencies related to loops. We improve the quality of that information by reusing the information given by the programmer for parallelization. We have implemented a prototype based on GCC into which we also add a new optimization pass. Our approach improves the amount of correctly classified dependencies resulting in 46% average improvement in single-thread performance for kernel benchmarks compared to GCC 6.1.

Debugging is a critical step in the development of any parallel program. However, the traditional interactive debugging model, where users manually step through code and inspect their application, does not scale well even for current supercomputers due its centralized nature. While lightweight debugging models, which have been proposed as an alternative, scale well, they can currently only debug a subset of bug classes. We therefore propose a new model, which we call prescriptive debugging, to fill this gap between these two approaches. This user-guided model allows programmers to express and test their debugging intuition in a way that helps to reduce the error space. Based on this debugging model we introduce a prototype implementation embodying this model, the DySectAPI, allowing programmers to construct probe trees for automatic, event-driven debugging at scale. In this paper we introduce the concepts behind DySectAPI and, using both experimental results and analytical modelling, we show that the DySectAPI implementation can run with a low overhead on current systems. We achieve a logarithmic scaling of the prototype and show predictions that even for a large system the overhead of the prescriptive debugging model will be small.