Improving the Performance of Actors on Multi-cores with Parallel Patterns

The Actor-based programming model is largely used in the context of distributed systems for its message-passing semantics and neat separation between the concurrency model and the underlying hardware platform. However, in the context of a single multi-core node where the performance metric is the primary optimization objective, the “pure” Actor Model is generally not used because Actors cannot exploit the physical shared-memory, thus reducing the optimization options. In this work, we propose to enrich the Actor Model with some well-known Parallel Patterns to face the performance issues of using the “pure” Actor Model on a single multi-core platform. In the experimental study, conducted on two different multi-core systems by using the C++ Actor Framework, we considered a subset of the Parsec benchmarks and two Savina benchmarks.The analysis of results demonstrates that the Actor Model enriched with suitable Parallel Patterns implementations provides a robust abstraction layer capable of delivering performance results comparable with those of thread-based libraries (i.e. Pthreads and FastFlow) while offering a safer and versatile programming environment.