PHAST: Hardware-Accelerated Shortest Path Trees

Delling, Daniel; Goldberg, Andrew V.; Nowatzyk, Andreas; Werneck, Renato F.

doi:10.1109/ipdps.2011.89

Cited by 64 publications

(60 citation statements)

References 22 publications

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“…However, the preprocessing of Arc-Flags is, in general, very time-consuming. In the literature, several strategies have been proposed to precompute the Arc-Flags, which we briefly describe in what follows; for more details we refer to [12,22]. AF1 [22]: a full shortest path tree rooted at each boundary node of each region is built in the reverse graph, by running Dijkstra's algorithm.…”

Section: Arc-flagsmentioning

confidence: 99%

“…PHAST [12]: a full shortest path tree rooted at each boundary node of each region is built by running PHAST (Parallel Hardware-Accelerated Shortest path Trees), a family of algorithms proposed in [12] which take advantage of features of modern CPU architectures, such as multi-core, thus requiring a parallel setup. In [12], a sequential version of PHAST has also been proposed.…”

Section: Arc-flagsmentioning

confidence: 99%

“…The pseudocode of DecrementalUpdate is given in Figure 2. The procedure uses a binary heap H, which is filled during the first computation of shortest paths (lines [3][4][5][6][7][8][9][10][11][12][13] and it is used during the second computation (lines [14][15][16][17][18][19][20][21][22] to extract the nodes in a greedy order, mimicking Dijkstra's algorithm. The cycle at lines 1-22, considers only boundary nodes b belonging to the Road-Signs of edge (x i , y i ).…”

Section: Decremental Algorithmmentioning

confidence: 99%

“…PHAST [12] consists of a family of algorithms suitable for the computation from scratch of Arc-Flags. All these algorithms are able to exploit locality and perform very well in graphs with low-highway dimension [3], such as road networks.…”

Section: Evaluation Of the Update Phasementioning

confidence: 99%

“…As our implementation of Road-Signs and DynamicRS is sequential, we do not consider them in our experimental evaluation and leave open the problem of parallelizing Road-Signs and DynamicRS and to compare their practical performance with the parallel versions of PHAST. On the other hand, it has been shown [12] that the single-processor version of PHAST is, on average, slightly more than an order of magnitude faster than Dijkstra (by about a factor of 16.5).…”

Section: Evaluation Of the Update Phasementioning

confidence: 99%

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Fully dynamic update of arc-flags

2014

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Best connections in real networks are usually found by applying Dijkstra's shortest paths algorithm. Unfortunately, networks deriving from real-world applications are huge, yielding unsustainable times to compute shortest paths. Therefore, considerable research has been conducted in recent years to accelerate Dijkstra's algorithm on typical instances of transportation and communication networks, such as road networks. These efforts have led to the development of many so called speed-up techniques, as for example Arc-Flags. The main drawback of many of these techniques, including Arc-Flags, is that they do not work well in the realistic dynamic scenarios where the networks change over time. In this article, we introduce a new data structure, named Road-Signs, which is used to update the Arc-Flags of a graph in fully dynamic scenarios. Road-Signs can be used to compute Arc-Flags, can be efficiently updated and does not require large space consumption for sparse networks. We develop a fully dynamic algorithm for updating Arc-Flags, by updating Road-Signs, each time that a modification occurs on an edge of the network. We show that this algorithm is better than recomputation from scratch of Arc-Flags in terms of the affected parameters of the input, which makes this solution suitable to be efficient in practice. However, it is not better than recomputation from scratch in the worst case. We also propose an experimental study to evaluate the practical performance of the new dynamic algorithm. To this aim, we use real-world road networks subject to sequences of weight change operations. Our experiments show a significant speed-up in the updating phase with respect to the recomputation from scratch of Arc-Flags.

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Section: Arc-flagsmentioning

confidence: 99%

Section: Arc-flagsmentioning

confidence: 99%

Section: Decremental Algorithmmentioning

confidence: 99%

Section: Evaluation Of the Update Phasementioning

confidence: 99%

Section: Evaluation Of the Update Phasementioning

confidence: 99%

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Fully dynamic update of arc-flags

2014

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Trajectory Analysis for Driving

Krumm

2011

Computing With Spatial Trajectories

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Customizing Driving Directions with GPUs

Delling

Kobitzsch

Werneck

2014

Lecture Notes in Computer Science

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Computing driving directions interactively on continental road networks requires preprocessing. This step can be costly, limiting our ability to incorporate new optimization functions, including traffic information or personal preferences. We show how the performance of the state-of-the-art customizable route planning (CRP) framework is boosted by GPUs, even though it has highly irregular structure. Our experimental study reveals that our method is an order of magnitude faster than a highly-optimized parallel CPU implementation, enabling interactive personalized driving directions on continental scale.

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PHAST: Hardware-Accelerated Shortest Path Trees

Cited by 64 publications

References 22 publications

Fully dynamic update of arc-flags

Fully dynamic update of arc-flags

Trajectory Analysis for Driving

Customizing Driving Directions with GPUs

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