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The actor model provides high-level concurrency abstractions to coordinate simultaneous computations by message passing. Languages implementing the actor model such as Erlang commonly only support single-headed pattern matching over received messages. We propose and design an extension of Erlang style actors with receive clauses containing multi-headed message patterns. Patterns may be non-linear and constrained by guards. We provide a number of examples to show the usefulness of the extension. We also explore the design space for multi-headed message matching semantics, for example first-match and rule priority-match semantics. The various semantics are inspired by the multi-set constraint matching semantics found in Constraint Handling Rules. This provides us with a formal model to study actors with multiheaded message receive patterns. The system can be implemented efficiently and we have built a prototype as a library-extension to Haskell.

Constraint Handling Rules (CHR) is a concurrent committedchoice constraint logic programming language to describe transformations (rewritings) among multi-sets of constraints (atomic formulae). CHR is widely used in a range of applications spanning from type system design to artificial intelligence. However, none of the existing CHR implementations we are aware of exploits concurrency or parallelism explicitly. We give a concurrent CHR implementation using GHC (Glasgow Haskell Compiler) with support for Software Transactional Memory. We achieve some significant performance improvements compared to a single-threaded CHR implementation. We obtain a further speed-up, in some cases nearly close to the optimum of 100%, when running programs under under a dual-core processor architecture. Our results show that CHR can be implemented efficiently on a multi-core architecture.

CHR is a declarative, concurrent and committed choice rule-based constraint programming language. In this paper, we adapt CHR to provide a decentralized execution model for parallel and distributed programs. Specifically, we consider an execution model consisting of an ensemble of computing entities, each with its own constraint store and each capable of communicating with its neighbors. We extend CHR into CHR e , in which rewrite rules are executed at one location and are allowed to access the constraint store of its immediate neighbors. We give an operational semantics for CHR e , denoted ω e 0 , that defines incremental and asynchronous decentralized rewriting for the class of CHR e rules characterized by purely local matching (0-neighbor restricted rules). We show the soundness of the ω e 0 semantics with respect to the abstract CHR semantics. We then give a safe encoding of the more general 1-neighbor restricted rules as 0-neighbor restricted rules, and discuss how this encoding can be generalized to all CHR e programs. * Funded by the Qatar National Research Fund as project NPRP 09-667-1-100 (Effective Programming for Large Distributed Ensembles)

Multi-set constraint rewriting allows for a highly parallel computational model and has been used in a multitude of application domains such as constraint solving, agent specification etc. Rewriting steps can be applied simultaneously as long as they do not interfere with each other. We wish that the underlying constraint rewrite implementation executes rewrite steps in parallel on increasingly popular becoming multi-core architectures. We design and implement efficient algorithms which allow for the parallel execution of multi-set constraint rewrite rules. Our experiments show that we obtain some significant speed-ups on multi-core architectures.