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.

中文翻译：

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使用并行模式在多核上提高 Actor 的性能

基于 Actor 的编程模型主要用于分布式系统的上下文中，因为它具有消息传递语义以及并发模型和底层硬件平台之间的巧妙分离。但是，在性能指标是主要优化目标的单个多核节点的上下文中，通常不使用“纯”Actor 模型，因为 Actor 无法利用物理共享内存，从而减少了优化选项。在这项工作中，我们建议用一些众所周知的并行模式来丰富 Actor 模型，以解决在单个多核平台上使用“纯”Actor 模型的性能问题。在使用 C++ Actor 框架在两个不同的多核系统上进行的实验研究中，我们考虑了 Parsec 基准测试的一个子集和两个 Savina 基准测试。