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MULTILISP: a language for concurrent symbolic computation

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Halstead, Robert H.·1985·ACM Transactions on Programming Languages and Systems

Abstract

Multilisp is a version of the Lisp dialect Scheme extended with constructs for parallel execution. Like Scheme, Multilisp is oriented toward symbolic computation. Unlike some parallel programming languages, Multilisp incorporates constructs for causing side effects and for explicitly introducing parallelism. The potential complexity of dealing with side effects in a parallel context is mitigated by the nature of the parallelism constructs and by support for abstract data types: a recommended Multilisp programming style is presented which, if followed, should lead to highly parallel, easily understandable programs. Multilisp is being implemented on the 32-processor Concert multiprocessor; however, it is ultimately intended for use on larger multiprocessors. The current implementation, called Concert Multilisp , is complete enough to run the Multilisp compiler itself and has been run on Concert prototypes including up to eight processors. Concert Multilisp uses novel techniques for task scheduling and garbage collection. The task scheduler helps control excessive resource utilization by means of an unfair scheduling policy; the garbage collector uses a multiprocessor algorithm based on the incremental garbage collector of Baker.

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Metadata

Authors
Halstead, R. H.
Venue
ACM Transactions on Programming Languages and Systems
Published
1985-10
Volume
7
Issue
4
Pages
501-538
ISSN
  • 0164-0925 (ISSN-L)
  • 1558-4593
OpenAlex
W1983587324
Cited by
1,073
References
0
FWCI
25.15
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: MULTILISP: a language for concurrent symbolic computation. https://app.cochat.ai/papers/10.1145/4472.4478
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Halstead, R. H. (1985). MULTILISP: a language for concurrent symbolic computation. ACM Transactions on Programming Languages and Systems. https://doi.org/10.1145/4472.4478

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.