An Online Algorithm for Dimension-Bound Analysis
Euro-Par '99 Proceedings of the 5th International Euro-Par Conference on Parallel Processing
A framework algorithm for dynamic, centralized dimension-bounded timestamps
CASCON '00 Proceedings of the 2000 conference of the Centre for Advanced Studies on Collaborative research
A low-overhead dedicated execution support for stream applications on shared-memory cmp
Proceedings of the tenth ACM international conference on Embedded software
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The vector-clock size necessary to characterize causality in a distributed computation is bounded by the dimension of the partial order induced by that computation. In an arbitrary distributed computation the dimension can be as large as the width, which in turn can be as large as the number of processes in the computation. Most vector clock algorithms, and all online ones, simply use a vector of size equal to the number of processes. In practice the dimension may be much smaller. It is the purpose of this paper to provide empirical evidence that the dimension of various distributed computations is often substantially smaller than the number of processes. We have found that typical distributed computations, with as many as 300 processes, have dimension less than 10. To achieve this quantification we developed various theorems and algorithms which we also describe.