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

Authors: Kazunori Ueda;

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

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 meta-interpreter 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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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average