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To Parse or Not To Parse

Authors: Vanhoof, Wim; Martens, Bern;

To Parse or Not To Parse

Abstract

In this paper, we reconsider the problem of specialising the vanilla meta interpreter through fully automatic and completely general partial deduction techniques. In particular, we study how the homeomorphic embedding relation guides specialisation of the interpreter. We focus on the so-called parsing problem, i.e. removing all parsing overhead from the program, and demonstrate that further refinements in the control of general partial deduction are necessary to properly deal with it. In particular, we modify local control on the basis of information imported from the global level. The resulting control strategy, while remaining fully general, leads to excellent specialisation of vanilla like meta programs. Parsing is always specialised, but — appropriately, as we will show — not always completely removed. As a concrete application, we subject an extended vanilla meta interpreter capable of dealing with compositions of programs to our techniques, showing we equal or surpass results obtained through a more ad hoc approach.

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    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).
    12
    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.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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citations
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!
12
Average
Average
Top 10%
Green