Evaluation of OpenMP Dependent Tasks with the KASTORS Benchmark Suite

The recent introduction of task dependencies in the OpenMP specification provides new ways of synchronizing tasks. Application programmers can now describe the data a task will read as input and write as output, letting the runtime system resolve fine-grain dependencies between tasks to decide which task should execute next. Such an approach should scale better than the excessive global synchronization found in most OpenMP 3.0 applications. As promising as it looks however, any new feature needs proper evaluation to encourage application programmers to embrace it. This paper introduces the KASTORS benchmark suite designed to evaluate OpenMP tasks dependencies. Wemodified state-of-theart OpenMP 3.0 benchmarks and data-flow parallel linear algebra kernels to make use of tasks dependencies. Learning from this experience, we propose extensions to the current OpenMP specification to improve the expressiveness of dependencies. We eventually evaluate both the GCC/libGOMP and the CLANG/libIOMP implementations of OpenMP 4.0 on our KASTORS suite, demonstrating the interest of task dependencies compared to taskwait-based approaches.