Automatic Derivation of Code Generators from Machine Descriptions

Cattell, R. G. G.

doi:10.1145/357094.357097

Cited by 105 publications

(34 citation statements)

References 6 publications

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“…We want to generate the code generator (i.e., instruction selector) directly from an ISA description. This is somewhat similar to Cattell's dissertation work [5], which has apparently not been followed up. However, we also want to derive the costs in the rules automatically from machine timing descriptions.…”

Section: The Needsupporting

confidence: 77%

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The CoGenT project: co-generating compilers and simulators for dynamically compiled languages

Moss

Weems

Richards

Proceedings International Parallel and Distributed Processing Symposium

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Section: The Needsupporting

confidence: 77%

“…However, in our case we want to generate the rules from a description of the semantics of the ISA. This is more or less the problem that Cattell studied [5], and is also similar to the MLRISC back-end strategy [9]. (There are other such systems, but these are representative.)…”

Section: Generating Compiler Componentsmentioning

confidence: 99%

The CoGenT project: co-generating compilers and simulators for dynamically compiled languages

Moss

Weems

Richards

Proceedings International Parallel and Distributed Processing Symposium

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“…The construction of the code generator and the code-generator generator in PQCC are reported by Cattell in [4,5,6]. Cattell uses a means-ends analysis to determine an optimal code match.…”

Section: Other Workmentioning

confidence: 99%

“…5 We now apply the A search algorithm with the cost function f (n) = g(n) + h (n) to our running example. We begin by generating the (3) successors of the initial node, as shown in Example 4.3.…”

Section: Example 44mentioning

confidence: 99%

Code generation = A* + BURS

Nymeyer

Katoen

Westra

et al. 1996

Lecture Notes in Computer Science

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Abstract.A system called BURS that is based on term rewrite systems and a search algorithm A are combined to produce a code generator that generates optimal code. The theory underlying BURS is re-developed, formalised and explained in this work. The search algorithm uses a cost heuristic that is derived from the term rewrite system to direct the search. The advantage of using a search algorithm is that we need to compute only those costs that may be part of an optimal rewrite sequence.Key words: compiler generators, code generation, term rewrite systems, search algorithms, formal techniquesCompiler building is a time-consuming and error-prone activity. Building the frontend (i.e. scanner, parser and intermediate-code generator) is straightforward-the theory is well established, and there is ample tool support. The main problem lies with the back-end, namely the code generator and optimiser-there is little theory and even less tool support. Generating a code generator from an abstract specification, also called automatic code generation, remains a very difficult problem.Pattern matching and selection is a general class of code-generation technique that has been studied in many forms. The most successful form uses a code generator that works predominantly bottom-up; a so-called bottom-up pattern matcher (BUPM). A variation of this technique is based on term rewrite systems. This technique, popularised under the name BURS, and developed by Pelegrí-Llopart and Graham [31], has arguably been considered the state of the art in automatic code generation. BURS, which stands for bottom-up rewrite system, has an underlying theory that is poorly understood. The theory has received virtually no attention in the literature since its initial publication [30]. The only research that has been carried out in this technique has been on improved tablecompression methods. Many researchers who claim to use BURS theory (e.g. [32,14]) generally use 'weaker' tree grammars instead of term rewrite systems, or equate BURS with a system that does a static cost analysis (e.g. [13]). We argue that a static cost analysis is neither necessary nor sufficient to warrant a BURS label, and that a system that is based on tree grammars cannot be a BURS.In this work we present an outline of formal BURS theory. Due to space restrictions, we present the full theory in [29]. This formalisation of BURS contrasts with the semiformal work of Pelegrí-Llopart and Graham. But there are other important differences in our work. We do not, for example, use instruction costs to do static pattern selection, and we do not use dynamic programming. Instead we use a heuristic search algorithm that only needs to dynamically compute costs for those patterns that may contribute to ?

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“…Finally, YC's portability suggests using its optimizer with, or as an alternative to, the new pattern-matching code generators [6,13,16,17]. Each of these systems works differently, but most match intermediate code with instruction patterns and pick the one that maximizes some measure.…”

Section: Related Wori~mentioning

confidence: 99%

Code selection through object code optimization

Davidson

Fraser

1984

ACM Trans. Program. Lang. Syst.

104

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This paper shows how thorough object code optimization has simplified a compiler and made it easy to retarget. The code generator forgoes case analysis and emits naive code that is improved by a retargetable object code optimizer. With this technique, cross-compilers have been built for seven machines, some in as few as three person days. These cross-compilers emit code comparable to hostspecific compilers.

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Automatic Derivation of Code Generators from Machine Descriptions

Cited by 105 publications

References 6 publications

The CoGenT project: co-generating compilers and simulators for dynamically compiled languages

The CoGenT project: co-generating compilers and simulators for dynamically compiled languages

Code generation = A* + BURS

Code selection through object code optimization

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