SALSA lite: A hash-based actor runtime for efficient local concurrency

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Abstract

As modern computer processors continue becoming more parallel, the actor model plays an increasingly important role in helping develop correct concurrent systems. In this paper, we consider efficient runtime strategies for non-distributed actor programming languages. While the focus is on a non-distributed implementation, it serves as a platform for a future efficient distributed implementation. Actors extend the object model by combining state and behavior with a thread of control, which can significantly simplify concurrent programming. Further, with asynchronous communication, no shared memory, and the fact an actor only processes one message at a time, it is possible to easily implement transparent distributed message passing and actor mobility. This paper discusses SALSA Lite, a completely re-designed actor runtime system engineered to maximize performance. The new runtime consists of a highly optimized core for lightweight actor creation, message passing, and message processing, which is used to implement more advanced coordination constructs. This new runtime is novel in two ways. First, by default the runtime automatically maps the lightweight actors to threads, allowing the number of threads used by a program to be specified at runtime transparently, without any changes to the code. Further, language constructs allow programmers to have first class control over how actors are mapped to threads (creating new threads if needed). Second, the runtime directly maps actor garbage collection to object garbage collection, allowing non-distributed SALSA programs to use Java’s garbage collection “for free”. This runtime is shown to have comparable or better performance for basic actor constructs (message passing and actor creation) than other popular actor languages: Erlang, Scala, and Kilim.

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APA

Desell, T., & Varela, C. A. (2014). SALSA lite: A hash-based actor runtime for efficient local concurrency. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8665, 144–166. https://doi.org/10.1007/978-3-662-44471-9_7

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