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A complexity measure for data flow models

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Abstract

For common data flow schemes, the number of copies of tokens made during a computation is shown to be a Blum complexity measure.(1) Results from abstract complexity theory (see Ref. 2) then hold for the copy measure, indicating, for example, that any implementation of a data flow processor will be constrained by its ability to copy tokens. The copy measure is a natural measure of complexity for data flow computations, and is distinct from the usual time or space measures. The result is generalized to a wider class of data flow schemas, including those with an apply operator. An example is also given of a data flow scheme which makes no copies.

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Authors and Affiliations

  1. Department of Computer Science, The University of Maryland, 20742, College Park, MD

    Howard E. Motteler & Carl H. Smith

Authors
  1. Howard E. Motteler
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  2. Carl H. Smith
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Additional information

Supported in part by NSF Grant MCS 83-01536 and NSA OCREAE Grant MDA904-85-H-0002.

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Motteler, H.E., Smith, C.H. A complexity measure for data flow models. International Journal of Computer & Information Sciences 14, 107–122 (1985). https://doi.org/10.1007/BF00996925

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  • DOI: https://doi.org/10.1007/BF00996925

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