Download Constraints Meet Concurrency by Jacopo Mauro (auth.) PDF

By Jacopo Mauro (auth.)

This booklet describes the advantages that emerge while the fields of constraint programming and concurrency meet. at the one hand, constraints can be utilized in concurrency idea to extend the conciseness and the expressive energy of concurrent languages from a practical perspective. nevertheless, difficulties modeled through the use of constraints should be solved swifter and extra successfully utilizing a concurrent process. either instructions are explored offering separate strains of improvement. to start with the expressive strength of a concurrent language is studied, particularly Constraint dealing with ideas, that helps constraints as a primitive build. The beneficial properties of this language which make it Turing strong are proven. Then a framework is proposed to resolve constraint difficulties that's meant to be deployed on a concurrent process. For the improvement of this framework the concurrent language Jolie following the carrier orientated paradigm is used. according to this event, an extension to carrier orientated Languages is usually proposed with a purpose to conquer a few of their barriers and to enhance the improvement of concurrent applications.

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Even when the dual approach of shared memory, namely message passing, the problem cannot be avoided since there will be race conditions over the network resource. The problem of race conditions has been widely studied in the literature and a lot of techniques have been developed to deal with it. Usually we use the term mutual exclusion to describe algorithms that are used to avoid the simultaneous use of a common resource. Starting from the 1960s, even hardware was design to allow the use of mutual exclusion algorithms that can be divided into two categories: • busy-wait solutions in which a process repeatedly checks to see if the resource is available.

3, while p is an arithmetic expression, with Fv( p) ⊆ (Fv(H k ) ∪ Fv(H h )), which expresses the priority of rule r . If Fv( p) = ∅ then p is a static priority, otherwise it is called dynamic. The formal semantics of CHRω p , defined by [75], is an adaptation of the traditional semantics to deal with rule priorities. Formally this semantics, denoted by ω p , is ωp a state transition system T = (Conf, → P ) where P is a CHRω p program while configurations in Conf, as well as the initial and final configurations, are the same as those introduced for the traditional semantics in Sect.

6 CHR with Priorities De Koninck et al. [75] extended CHR with user-defined priorities. This new language, denoted by CHRω p , provides a high-level alternative for controlling program execution, that is more appropriate to needs of CHR programmers than other low-level approaches. t. chr(H1 ) = θ H1≤ , chr(H2 ) = θ H2≤ , CT |= C → ∃−Fv(C) (θ ∧ D), θ p is a ground arithmetic expression and t = id(H1 ) ++ id(H2 ) ++ [r ] ∈ / T . Furthermore, no rule of priority p ≤ and substitution θ ≤ exists with θ ≤ p ≤ < θ p for which the above conditions hold Fig.

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