PHAST: Hardware-accelerated shortest path trees

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

doi:10.1016/j.jpdc.2012.02.007

Cited by 87 publications

(34 citation statements)

References 13 publications

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“…As in [17] we could expect about twice faster queries by combining CH-TNR with arc flags for an additional sense of goal direction. The additional preprocessing time could be much smaller than in [17] by using new CH based methods for fast parallel one-to-all shortest paths [24]. Local queries can be accelerated by introducing additional layers as in HH-TNR.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, while the following numbers are encouraging, they have to be taken with a grain of salt. First, we briefly explain the layout of the query and reach for previous work by Bauer et al [20], Delling [21] and Abraham et al [22] to conduct the preprocessing. Second, we note that the cost of a L1 cache miss is about 10 cycles and the cost of a L3 cache miss is about 100 cycles on a modern memory architecture.…”

Section: Pruning With Arc Flagsmentioning

confidence: 99%

“…Thus, we account for an additional 55 nanoseconds for each such access. If a local query CHASE query costs 6.1 microseconds on average [22] and is conducted for 0.581% of all queries, then the local searches account for roughly 100 nanoseconds on average. This amounts to an expected total query time on a fictive 2 GHz CPU of 250ns + 6 · 5ns + (6.1) 2 · 1ns + 3.1 · 55ns + 100ns 590ns, assuming that a single Voronoi-Locality filter invocation costs about 250 nanoseconds.…”

Section: Pruning With Arc Flagsmentioning

confidence: 99%

“…This is a conservative estimate since it does not account for any SIMD tuning opportunity. For CHASE preprocessing, recent work of Abraham et al [22] gives an efficient algorithm, called PHAST, to compute arc flags on our test instance in mere 14 minutes of preprocessing including CH construction. The additional memory overhead amounts to roughly 600 MB.…”

Section: Pruning With Arc Flagsmentioning

confidence: 99%

See 3 more Smart Citations

Transit Node Routing Reconsidered

Arz

Luxen

Sanders

2013

Lecture Notes in Computer Science

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Abstract. Transit Node Routing (TNR) is a fast and exact distance oracle for road networks. We show several new results for TNR. First, we give a surprisingly simple implementation fully based on Contraction Hierarchies that speeds up preprocessing by an order of magnitude approaching the time for just finding a Contraction Hierarchies (which alone has two orders of magnitude larger query time). We also develop a very effective purely graph theoretical locality filter without any compromise in query times. Finally, we show that a specialization to the online many-to-one (or one-to-many) shortest path further speeds up query time by an order of magnitude. This variant even has better query time than the fastest known previous methods which need much more space.

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Section: Discussionmentioning

confidence: 99%

Section: Pruning With Arc Flagsmentioning

confidence: 99%

Section: Pruning With Arc Flagsmentioning

confidence: 99%

Section: Pruning With Arc Flagsmentioning

confidence: 99%

See 2 more Smart Citations

Transit Node Routing Reconsidered

Arz

Luxen

Sanders

2013

Lecture Notes in Computer Science

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“…In weighted networks, the Dijkstra algorithm should be used to solve the single-source shortest path (SSSP) problem rather than the breadth-first search (BFS) algorithm. In previous work, many efforts have been devoted to developing a GPU version of the SSSP problem using the well-known Dijkstra algorithm (Martin, Torres & Gavilanes, 2009;Ortega-Arranz et al, 2013;Delling et al, 2011;Davidson et al, 2014). Although such algorithms have been successfully developed and presented, establishing a parallel version of the BC algorithm for weighted networks is nontrivial because the original SSSP algorithm must be modified in many critical aspects for this task, and to the best of our knowledge, a suitable fast solution is still lacking.…”

Section: Introductionmentioning

confidence: 99%

A GPU-based solution for fast calculation of the betweenness centrality in large weighted networks

Fan

Zhao

2017

PeerJ Computer Science

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Betweenness, a widely employed centrality measure in network science, is a decent proxy for investigating network loads and rankings. However, its extremely high computational cost greatly hinders its applicability in large networks. Although several parallel algorithms have been presented to reduce its calculation cost for unweighted networks, a fast solution for weighted networks, which are commonly encountered in many realistic applications, is still lacking. In this study, we develop an efficient parallel GPU-based approach to boost the calculation of the betweenness centrality (BC) for large weighted networks. We parallelize the traditional Dijkstra algorithm by selecting more than one frontier vertex each time and then inspecting the frontier vertices simultaneously. By combining the parallel SSSP algorithm with the parallel BC framework, our GPU-based betweenness algorithm achieves much better performance than its CPU counterparts. Moreover, to further improve performance, we integrate the work-efficient strategy, and to address the loadimbalance problem, we introduce a warp-centric technique, which assigns many threads rather than one to a single frontier vertex. Experiments on both realistic and synthetic networks demonstrate the efficiency of our solution, which achieves 2.9Â to 8.44Â speedups over the parallel CPU implementation. Our algorithm is open-source and free to the community; it is publicly available through https://dx.doi.org/10.6084/m9.figshare.4542405. Considering the pervasive deployment and declining price of GPUs in personal computers and servers, our solution will offer unprecedented opportunities for exploring betweenness-related problems and will motivate follow-up efforts in network science.

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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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PHAST: Hardware-accelerated shortest path trees

Cited by 87 publications

References 13 publications

Transit Node Routing Reconsidered

Transit Node Routing Reconsidered

A GPU-based solution for fast calculation of the betweenness centrality in large weighted networks

Fully dynamic update of arc-flags

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