A new GPU-based approach to the Shortest Path problem

Ortega–Arranz, Hector; Torres, Yuri; Llanos, Diego R.; González-Escribano, Arturo

doi:10.1109/hpcsim.2013.6641461

Cited by 41 publications

(37 citation statements)

References 13 publications

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“…Moreover, these settled nodes satisfy the level condition, and because of this, they form a new level to be inspected simultaneously in the dependency accumulation step. In this paper, we apply the method described in Ortega- Arranz et al (2013). In this method,…”

Section: Parallel Dijkstra Algorithmmentioning

confidence: 99%

“…Meanwhile, a Kronecker graph possesses scale-free and small-world characteristics, making it appropriate for studying the load-imbalance problem. We uniformly assigned random edge weights ranging from 1 to 10, as done in previous studies (Martin, Torres & Gavilanes, 2009;Ortega-Arranz et al, 2013). We used these synthetic networks to study the relationship between the graph structure and the traversal strategy.…”

Section: Experiments Network and Settingsmentioning

confidence: 99%

“…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%

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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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Section: Parallel Dijkstra Algorithmmentioning

confidence: 99%

Section: Experiments Network and Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…al. [5] has solved SSSP problem by using Dijkstra's algorithm. The complexity time of this algorithm is O(v2).…”

Section: Literature Reviewmentioning

confidence: 99%

A Review: Parallel Implementation of Shortest path Algorithm on GPGPU

Katkar¹,

Kulkarni²

2017

International Journal of Advanced Research in Computer and Comm

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To find out the shortest paths from a single source to all other vertices's is a common problem in graph analysis. The Bellman¬Ford's algorithm is solves such a single¬source shortestpath (SSSP) problem and better applies to beparallelized for many¬core architectures. TheSequential Bellman-Ford Algorithm is that it very time consuming for large data set & it's time complexity is O(ve). Therefore, frontier based Bellman¬Ford's algorithm significantly reduces the number of relaxation operation, number of iterations and time required for search task. This paper presents a parallel implementation of the Bellman¬Ford algorithm based on frontier propagation. General Purpose Graphics Processing Units (GPGPU) based parallel computing is the best alternate way to speed up the search task. This paper gives detailed information about frontier based Bellman¬Ford's algorithm to solve optimization problem & improve the performance of GPU based computing.

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“…The paper [7] proposes parallel Moore SSSP algorithm which based on the storage of source node to the nodes of the array D, queue elements of an array Q, parallelize the process of search and accelerate the algorithm to obtain the ideal results. There are also some other improved parallelization strategies for the optimization of the shortest path algorithm [8][9][10][11].…”

Section: Parallel Shortest Path Algorithmmentioning

confidence: 99%

MRI Image Segmentation Based on a GPU Shortest Path Algorithm

Wang¹,

Chen²

2015

Proceedings of the 2nd International Conference on Intelligent Computing and Cognitive Informatics

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Abstract-Dijkstra algorithm can be adopted to distinguish different parts of boundaries in medical image segmentation problem, which can be a reference for further segmentation operation. However, classical Dijkstra algorithm can hardly adapt to real time image segmentation problem owing to its exponentially O(n 2 ) computing complexity, especially for the increasing number of nodes. In this paper, we designed and implemented a parallel shortest path algorithm accelerated by GPU for medical image segmentation problem. A dynamic relax approach is presented to optimize classical Dijkstra algorithm. Therefore, the new parallel Dijkstra algorithm can be easily applied to CUDA parallel framework without concerning about GPU hardware and CUDA optimize details. Two experiments have been conducted to evaluate the algorithm performance. The results show that our new Dijkstra algorithm can get 8 speedup for 4096 points compared with the classical Dijkstra algorithm. Besides, an impressing result with two speed up for 128*128 points problems is demonstrated in parallel Dijkstra compared with parallel Moore. In conclusion, the new parallel Dijkstra algorithm can significantly improve the real-time performance of image segmentation.

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A new GPU-based approach to the Shortest Path problem

Cited by 41 publications

References 13 publications

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

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

A Review: Parallel Implementation of Shortest path Algorithm on GPGPU

MRI Image Segmentation Based on a GPU Shortest Path Algorithm

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