Generating efficient code for lazy functional languages

Smetsers, Sjaak; Nöcker, Eric; Groningen, John van; Plasmeijer, Rinu

doi:10.1007/3540543961_28

Cited by 18 publications

(10 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Optimisations such a s g a rbage collection, the use o f s tacks, o verwriting of n o d e s , p e r forming calculations on a special value stack etc. are a ll validated against the graph rewriting semantics (Barendregt et al (1987), Smetsers et al (1991)). …”

Section: Implementation Optimisationsmentioning

confidence: 99%

Graph rewriting semantics for functional programming languages

Eekelen

Smetsers

Plasmeijer

1997

Computer Science Logic

Self Cite

View full text Add to dashboard Cite

Abstract. The lambda calculus forms without any q uestion *the* theoretical backbone o f f u n ctional programming languages. For the design and implementation of the lazy functional language Concurrent C l e a n w e have used a related computational model: Term Graph Rewriting Systems (TGRS's). T h is paper wraps up our main conclusions after 10 years of experience with graph rewriting semantics for functional programming languages. TGRS's are not a direct extension of the l a m bdacalculus, so one sometimes has to re-establish known theoretical results. But T G R S 's are that much closer to the world of functional programming t h at its use has been proven to be very worthwhile. In TGRS's functions h a ve names, there are constants, pattern matching and one can choose to either share expressions or copy t h e m . Graph reduction very accurately models the essential behaviour o f m ost implementations of functional languages and therefore it forms a g o o d base for reasoning about reduction properties as well as the time and space consumption of functional applications. With uniqueness typing important information can be derived for ecient i m plementation and for purely functional interfacing w i t h i m perative programs.

show abstract

Section: Implementation Optimisationsmentioning

confidence: 99%

Graph rewriting semantics for functional programming languages

Eekelen

Smetsers

Plasmeijer

1997

Computer Science Logic

Self Cite

View full text Add to dashboard Cite

show abstract

“…Strictness analysis plays an important role in compilers for non-strict languages, enabling the compiler to determine cases where function arguments can be passed in evaluated form, which is often more e cient. Using this technology compilers for lazy languages can generate code which is sometimes as fast as or faster than C (Smetsers et al 1991]). Usually the results of strictness analysis are passed to the code generator, which is thereby made signi cantly more complicated.…”

Section: Source Language and Compilation Routementioning

confidence: 99%

Implementing lazy functional languages on stock hardware: the Spineless Tagless G-machine

Jones

1992

J. Funct. Prog.

226

View full text Add to dashboard Cite

The Spineless Tagless G-machine is an abstract machine designed to support non-strict higher-order functional languages. This presentation of the machine falls into three parts. Firstly, we give a general discussion of the design issues involved in implementing non-strict functional languages. Next, we present the STG language, an austere but recognizably-functional language, which as well as a denotational meaning has a well-defined operational semantics. The STG language is the ‘abstract machine code’ for the Spineless Tagless G-machine. Lastly, we discuss the mapping of the STG language onto stock hardware. The success of an abstract machine model depends largely on how efficient this mapping can be made, though this topic is often relegated to a short section. Instead, we give a detailed discussion of the design issues and the choices we have made. Our principal target is the C language, treating the C compiler as a portable assembler.

show abstract

“…FCG produces efficient code that supports two-space copying garbage collection in combination with divide and conquer parallelism [37]. In contrast to other functional language compilers that generate assembly directly [30,43,57], FCG uses the C compiler for target code generation, providing high-quality code optimisations and portability. The input language and the output language of FCG are thus both C. The generated code uses tagged data values and an explicit call stack to support garbage collection and parallel reduction (Section 7).…”

Section: The Fc G Code Generatormentioning

confidence: 99%

Experience with a clustered parallel reduction machine

Beemster

Hartel

Hertzberger

et al. 1993

Future Generation Computer Systems

View full text Add to dashboard Cite

A clustered architecture has been designed to exploit divide and conquer parallelism in functional programs. The programming methodology developed for the machine is based on explicit annotations and program transformations. It has been successfully applied to a number of algorithms resulting in a benchmark of small and m edium size parallel functional programs. Sophisticated compilation techniques are used such as strictness analysis on non-flat domains and R IS C and V L IW code generation. Parallel jobs are distributed by an efficient hierarchical scheduler. A special processor for graph reduction has been designed as a basic building block for the machine. A prototype of a single cluster machine has been constructed with stock hardware. This paper describes the experience with the project and its current state.

show abstract

Generating efficient code for lazy functional languages

Cited by 18 publications

References 6 publications

Graph rewriting semantics for functional programming languages

Graph rewriting semantics for functional programming languages

Implementing lazy functional languages on stock hardware: the Spineless Tagless G-machine

Experience with a clustered parallel reduction machine

Contact Info

Product

Resources

About