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List processing in real time on a serial computer

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Baker, Henry G.·1978·Communications of the ACM

Abstract

A real-time list processing system is one in which the time required by the elementary list operations (e.g. CONS, CAR, CDR, RPLACA, RPLACD, EQ, and ATOM in LISP) is bounded by a (small) constant. Classical implementations of list processing systems lack this property because allocating a list cell from the heap may cause a garbage collection, which process requires time proportional to the heap size to finish. A real-time list processing system is presented which continuously reclaims garbage, including directed cycles, while linearizing and compacting the accessible cells into contiguous locations to avoid fragmenting the free storage pool. The program is small and requires no time-sharing interrupts, making it suitable for microcode. Finally, the system requires the same average time, and not more than twice the space, of a classical implementation, and those space requirements can be reduced to approximately classical proportions by compact list representation. Arrays of different sizes, a program stack, and hash linking are simple extensions to our system, and reference counting is found to be inferior for many applications.

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Metadata

Authors
Baker, H. G.
Venue
Communications of the ACM
Published
1978-04
Volume
21
Issue
4
Pages
280-294
ISSN
  • 0001-0782 (ISSN-L)
  • 1557-7317
OpenAlex
W2161531345
Cited by
592
References
0
FWCI
14.03
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: List processing in real time on a serial computer. https://app.cochat.ai/papers/10.1145/359460.359470
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Baker, H. G. (1978). List processing in real time on a serial computer. Communications of the ACM. https://doi.org/10.1145/359460.359470

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.