This paper considers the single source shortest path (SSSP) problem, which is the key for many applications such as navigation, mapping, routing, and social networking. Existing SSSP algorithms are designed mostly for shared-memory systems. Nevertheless, with the prevalence of diverse smart devices like drones, there is a growing interest in deploying SSSP algorithms over distributed computing systems so that they can run efficiently onboard of smart devices via Mobile Ad Hoc Computing or at the network edges via Mobile Edge Computing. In this paper, we introduce a communication-efficient ∆-stepping algorithm for distributed computing systems. The proposed algorithm is featured by 1) a message coordination architecture for reducing message exchanges between workers, 2) a pruning technique for reducing redundant computations, and 3) an aggregation technique for further reducing message exchanges when communication delay is significant. Theoretical analyses and experimental studies on real-world graph datasets demonstrate the promising performance of proposed algorithm.
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A Shared-Memory Parallel Algorithm for Updating Single-Source Shortest Paths in Large Dynamic Networks
Computing the single-source shortest path (SSSP) is one of the fundamental graph algorithms, and is used in many applications. Here, we focus on computing SSSP on large dynamic graphs, i.e. graphs whose structure evolves with time. We posit that instead of recomputing the SSSP for each set of changes on the dynamic graphs, it is more efficient to update the results based only on the region of change. To this end, we present a novel two-step shared-memory algorithm for updating SSSP on weighted large-scale graphs. The key idea of our algorithm is to identify changes, such as vertex/edge addition and deletion, that affect the shortest path computations and update only the parts of the graphs affected by the change. We provide the proof of correctness of our proposed algorithm. Our experiments on real and synthetic networks demonstrate that our algorithm is as much as 4X faster compared to computing SSSP with Galois, a state-of-the-art parallel graph analysis software for shared memory architectures. We also demonstrate how increasing the asynchrony can lead to even faster updates. To the best of our knowledge, this is one of the first practical parallel algorithms for updating networks on shared-memory systems, that is also scalable to large networks.
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- NSF-PAR ID:
- 10107624
- Date Published:
- Journal Name:
- 2018 IEEE 25th International Conference on High Performance Computing (HiPC)
- Page Range / eLocation ID:
- 245 to 254
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/HiPC.2018.00035
