Eventify: Event-Based Task Parallelism for Strong Scaling

Today's processors become fatter, not faster. However, the exploitation of these massively parallel compute resources remains a challenge for many traditional HPC applications regarding scalability, portability and programmability. To tackle this challenge, several parallel programming approaches such as loop parallelism and task parallelism are researched in form of languages, libraries and frameworks. Task parallelism as provided by OpenMP, HPX, StarPU, Charm++ and Kokkos is the most promising approach to overcome the challenges of ever increasing parallelism. The aforementioned parallel programming technologies enable scalability for a broad range of algorithms with coarse-grained tasks, e. g. in linear algebra and classical N-body simulation. However, they do not fully address the performance bottlenecks of algorithms with fine-grained tasks and the resultant large task graphs. Additionally, we experienced the description of large task graphs to be cumbersome with the common approach of providing in-, out- and inout-dependencies. We introduce event-based task parallelism to solve the performance and programmability issues for algorithms that exhibit fine-grained task parallelism and contain repetitive task patterns. With user-defined event lists, the approach provides a more convenient and compact way to describe large task graphs. Furthermore, we show how these event lists are processed by a task engine that reuses user-defined, algorithmic data structures. As use case, we describe the implementation of a fast multipole method for molecular dynamics with event-based task parallelism. The performance analysis reveals that the event-based implementation is 52 % faster than a classical loop-parallel implementation with OpenMP.