Elsevier

Theoretical Computer Science

Volume 190, Issue 2, 20 January 1998, Pages 211-239
Theoretical Computer Science

Contribution
A probabilistic view of Datalog parallelization

https://doi.org/10.1016/S0304-3975(97)00091-1Get rights and content
Under an Elsevier user license
open archive

Abstract

We explore an approach to developing Datalog parallelization strategies that aims at good expected rather than worst-case performance. To illustrate, we consider a very simple parallelization strategy that applies to all Datalog programs. We prove that this has very good expected performance under equal distribution of inputs. This is done using an extension of 0–1 laws adapted to this context. The analysis is confirmed by experimental results on randomly generated data.

Cited by (0)

1

This work was done while the author was affiliated with the Ecole Nationale Supérieure des Télécommunications (ENST), Paris, France.

2

Work performed in part while visiting ENST, and supported in part by the NSF under grant IRI-9221268.