We investigate the issue of stalling in the LogP model. In particular, we introduce a novel quantitative characterization of stalling, referred to as δ-stalling, which intuitively captures the realistic assumption that once the network's capacity constraint is violated, it takes some time (at most δ) for this information to propagate to the processors involved. We prove a lower bound that shows that LogP under δ-stalling is strictly more powerful than the stall-free version of the model where only strictly stall-free computations are permitted. On the other hand, we show that δ-stalling LogP with δ = L can be simulated with at most logarithmic slowdown by a BSP machine with similar bandwidth and latency values, thus extending the equivalence (up to logarithmic factors) between stall-free LogP and BSP argued in [1] to the more powerful L-stalling LogP. © 2000 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Bilardi, G., Herley, K. T., Pietracaprina, A., & Pucci, G. (2000). On stalling in LogP. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1800 LNCS, pp. 109–115). Springer Verlag. https://doi.org/10.1007/3-540-45591-4_13
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