Revisiting actor programming in C++ - Charousset, Dominik; Hiesgen, Raphael; Schmidt, Thomas C. (Computer languages, systems & structures, 2016)
The actor model of computation has gained significant popularity over the last decade. Its high level of abstraction makes it appealing for concurrent applications in parallel and distributed systems. However, designing a real-world actor framework that subsumes full scalability, strong reliability, and high resource efficiency requires many conceptual and algorithmic additives to the original model. In this paper, we report on designing and building CAF, the C++ Actor Framework. CAF targets at providing a concurrent and distributed native environment for scaling up to very large, high-performance applications, and equally well down to small constrained systems. We present the key specifications and design concepts—in particular a message-transparent architecture, type-safe message interfaces, and pattern matching facilities—that make native actors a viable approach for many robust, elastic, and highly distributed developments. We demonstrate the feasibility of CAF in three scenarios: first for elastic, upscaling environments, second for including heterogeneous hardware like GPUs, and third for distributed runtime systems. Extensive performance evaluations indicate ideal runtime at very low memory footprint for up to 64 CPU cores, or when offloading work to a GPU. In these tests, CAF continuously outperforms the competing actor environments Erlang, Charm++, SalsaLite, Scala, ActorFoundry, and even the raw message passing framework OpenMPI. •We analyze the problem and design space for actor programming in C++.•We find that type-safe message passing interfaces are important for the robustness of actor programs.•Pattern Matching as a DSL in C++ eases the definition of message handlers.•We introduce an according framework for actor programming in C++ (CAF).•Core algorithms and the scalable architecture are thoroughly discussed.•Benchmarking CAF against many other frameworks reveals overall performance benefits.