Within ARM's reach

Fokkink, Wan; Kamperman, Jasper F.T; Walters, Pum

doi:10.1145/291889.291903

Cited by 11 publications

(7 citation statements)

References 24 publications

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“…The design of the compiler was influenced by the experience gained in previous compiler activities within the ASF+SDF project itself [Dik 1989;Fokkink et al 1998;Hendriks 1991;Kamperman 1996;Walters 1997] as well as in various functional language and Prolog compiler projects elsewhere. The surveys [Hartel et al 1996] on functional language compilation and [van Roy 1993] on Prolog compilation were particularly helpful.…”

Section: Major Design Considerationsmentioning

confidence: 99%

“…These transformations would perhaps be appropriate in a formal semantics of ASF+SDF, but in a compiler they cause a bottleneck whose effect is hard to undo at a later stage. For this reason, our compiler does not generate code for the Abstract Rewrite Machine (ARM), which was originally developed for ASF+SDF and then used in the compiler for the equational programming language Epic [Fokkink et al 1998]. ARM is based on the notion of minimal term rewriting system (MTRS).…”

Section: Pitfalls In High-level Transformations and Abstract Machine mentioning

confidence: 99%

“…To obtain a deterministic matching automaton it uses the specificity order defined in [Fokkink et al 1998, Definition 2.2.1]. Rewrite rules with more specific left-hand sides take precedence over rules whose left-hand sides are more general.…”

Section: The Aterm Librarymentioning

confidence: 99%

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Compiling language definitions

Brand

Heering

Klint

et al. 2002

ACM Trans. Program. Lang. Syst.

149

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The ASF+SDF Meta-Environment is an interactive language development environment whose main application areas are definition of domain-specific languages, generation of program analysis and transformation tools, production of software renovation tools, and general specification and prototyping. It uses conditional rewrite rules to define the dynamic semantics and other tooloriented aspects of languages, so the effectiveness of the generated tools is critically dependent on the quality of the rewrite rule implementation.The ASF+SDF rewrite rule compiler generates C code, thus taking advantage of C's portability and the sophisticated optimization capabilities of current C compilers as well as avoiding potential abstract machine interface bottlenecks. It can handle large (10 000+ rule) language definitions and uses an efficient run-time storage scheme capable of handling large (1 000 000+ node) terms. Term storage uses maximal subterm sharing (hash-consing), which turns out to be more effective in the case of ASF+SDF than in Lisp or SML. Extensive benchmarking has shown the time and space performance of the generated code to be as good as or better than that of the best current rewrite rule and functional language compilers.

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Section: Major Design Considerationsmentioning

confidence: 99%

Section: Pitfalls In High-level Transformations and Abstract Machine mentioning

confidence: 99%

See 1 more Smart Citation

Compiling language definitions

Brand

Heering

Klint

et al. 2002

ACM Trans. Program. Lang. Syst.

149

View full text Add to dashboard Cite

show abstract

“…Besides the two properties for each rule (l, r) ∈ R stated in Definition 1, we assume that each variable v ∈ V occurs at most once in l, i.e., l is linear, meaning that l → r is left-linear [11]. When innermost (and relaxed innermost) rewriting is used, an MTRS with non-left-linear rules can be rewritten to one that only contains left-linear rules [30,31]. To do so, an…”

Section: Definition 2 (Substitution)mentioning

confidence: 99%

Innermost many-sorted term rewriting on GPUs

Eerd

Groote

Hijma

et al. 2023

Science of Computer Programming

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“…Simultaneously, we were developing a formalism for representing parse trees [42,43]. In addition, incompatible term formats were in use in various of our compiler projects [44]. Observing the similarities between all these incompatible term data types triggered the work on ATerms as described here.…”

Section: Historymentioning

confidence: 99%

Efficient annotated terms

Brand

Jong

Klint

et al. 2000

Softw: Pract. Exper.

138

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How do distributed applications exchange tree-like data structures? We introduce the abstract data type of Annotated Terms (ATerms), and discuss their design, implementation and application. A comprehensive procedural interface enables the creation and manipulation of ATerms in C or Java. The ATerm implementation is based on maximal subterm sharing and automatic garbage collection. A binary exchange format for the concise representation of ATerms (sharing preserved) allows the fast exchange of ATerms between applications. In a typical application -parse trees which contain considerable redundant information -less than two bytes are needed to represent a node in memory, and less than two bits are needed to represent it in binary format. The implementation of ATerms scales up to the manipulation of ATerms in the giga-byte range. We are currently upgrading the ATerm library to support 64-bit architectures as well. § Transparent in the sense that the result of most operations is independent of the annotations. This makes it easy to completely ignore annotations. Examples of the use of annotations include annotating parse trees with positional or typesetting information, and annotating abstract syntax trees with the results of type checking.

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Within ARM's reach

Abstract: A new compilation technique for left-linear term rewriting systems is presented, where rewrite rules are transformed into so-called minimal rewrite rules. These minimal rules have such a simple form that they can be viewed as instructions for an abstract rewriting machine ARM.

Cited by 11 publications

References 24 publications

Compiling language definitions

Compiling language definitions

Innermost many-sorted term rewriting on GPUs

Efficient annotated terms

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