Parlog: Parallel Programming in Logic

Clark, Keith; Gregory, Steven

doi:10.1007/978-1-4613-1989-4_6

Cited by 49 publications

(33 citation statements)

References 4 publications

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“…Relational Language [7], the first concrete proposal of a concurrent logic language, was followed by a succession of proposals, namely Concurrent Prolog [20], PARLOG [8] and Guarded Horn Clauses (GHC) [27]. KL1 [29], the Kernel Language of the Fifth Generation Computer Systems (FGCS) project [22], was designed based on GHC by featuring (among others) mapping constructs for concurrent processes.…”

Section: Concurrency and Logic Programmingmentioning

confidence: 99%

A Pure Meta-interpreter for Flat GHC, a Concurrent Constraint Language

Ueda

2002

Computational Logic: Logic Programming and Beyond

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Abstract. This paper discusses the construction of a meta-interpreter of Flat GHC, one of the simplest and earliest concurrent constraint languages. Meta-interpretation has a long history in logic programming, and has been applied extensively to building programming systems, adding functionalities, modifying operational semantics and evaluation strategies, and so on. Our objective, in contrast, is to design the pair of (i) a representation of programs suitable for code mobility and (ii) a pure interpreter (or virtual machine) of the represented code, bearing networked applications of concurrent constraint programming in mind. This is more challenging than it might seem; indeed, meta-interpreters of many programming languages achieved their objectives by adding small primitives into the languages and exploiting their functionalities. A metainterpreter in a pure, simple concurrent language is useful because it is fully amenable to theoretical support including partial evaluation. After a number of trials and errors, we have arrived at treecode, a groundterm representation of Flat GHC programs that can be easily interpreted, transmitted over the network, and converted back to the original syntax. The paper describes how the interpreter works, where the subtleties lie, and what its design implies. It also describes how the interpreter, given the treecode of a program, is partially evaluated to the original program by the unfold/fold transformation system for Flat GHC.

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Section: Concurrency and Logic Programmingmentioning

confidence: 99%

A Pure Meta-interpreter for Flat GHC, a Concurrent Constraint Language

Ueda

2002

Computational Logic: Logic Programming and Beyond

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“…Note that our techniques are directly applicable to committed-choice logic programming languages [3,21,24], which are intrinsically determínate: the committed choice nature of such languages also permits simplifications to (1) above. It may be possible to do better than this in some cases, e.g.…”

Section: Cost Estimationmentioning

confidence: 99%

Task granularity analysis in logic programs

1990

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While logic programming languages offer a great deal of scope for parallelism, there is usually some overhead associated with the execution of goals in parallel because of the work involved in task creation and scheduling. In practice, therefore, the "granularity" of a goal, i.e. an estimate of the work available under it, should be taken into account when deciding whether or not to execute a goal concurrently as a sepárate task. This paper describes a method for estimating the granularity of a goal at compile time. The runtime overhead associated with our approach is usually quite small, and the performance improvements resulting from the incorporation of grainsize control can be quite good. This is shown by means of experimental results.

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“…The inherent parallelism to the arithmetic operations called by the user, although appropriate to a static mapping because of its lower granularity, is then automatically exploited by the load-balancer. To express the dynamic paraUelization, several choices are possible [3,5,10]. As far as algebraic computation is concerned, mainly by independent AND parallelism (modular computations, distributed computations) and OR parallelism (probabilistic algorithms, several strategies with no deterministic choice) [11], we have chosen, in order to express parallelism, to use Fork (OR-parallelism) primitives and Fork-Join (AND-Parallelism) primitives, well suited for an effective computation.…”

Section: Load-balancing : High-level Specificationsmentioning

confidence: 99%

Cost prediction for load-balancing: Application to algebraic computations

Roch¹,

Vermeerbergen²,

Villard³

1992

Parallel Processing: CONPAR 92—VAPP V

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A major feature of Computer Algebra, and more generally of non-numerical computations, is the dynamical and non-predictable behaviour of the executions. We then understand that statical analysis should imperatively be completed by dynamical analysis in order to reach the best distribution of the tasks among the processors. In this paper, we present a new load-balancing system for parallel architectures with great numbers of processors. Being well suited for Computer Algebra and based on the notion of granularity, it is original in the sense that it takes into account the tasks complexity as a consistent information in order to achieve efficiency.

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Parlog: Parallel Programming in Logic

Cited by 49 publications

References 4 publications

A Pure Meta-interpreter for Flat GHC, a Concurrent Constraint Language

A Pure Meta-interpreter for Flat GHC, a Concurrent Constraint Language

Task granularity analysis in logic programs

Cost prediction for load-balancing: Application to algebraic computations

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