Hong-linh Truong, Maja Vukovic, et al.
ICDH 2024
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 Bilardi et al. (Algorithmica 24 (1999) 405) and Ramachandran et al. (J. Parallel Distributed Comput. 63 (2003) 1175) to the more powerful L-stalling LogP. © 2004 Elsevier Inc. All rights reserved.
Hong-linh Truong, Maja Vukovic, et al.
ICDH 2024
Victor Akinwande, Megan Macgregor, et al.
IJCAI 2024
John R. Kender, Rick Kjeldsen
IEEE Transactions on Pattern Analysis and Machine Intelligence
Merve Unuvar, Yurdaer Doganata, et al.
CLOUD 2014