Accelerating Large Graph Algorithms on the GPU Using CUDA

Harish, Pawan; Narayanan, P. J.

doi:10.1007/978-3-540-77220-0_21

Cited by 541 publications

(469 citation statements)

References 10 publications

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“…One of the earliest works on parallel shortest path problem was proposed by Micikevicius et al in [30] that was subsequently improved by Harish and Narayanan [16]. The algorithm of Floyd and Warshall (cf.…”

Section: Related Workmentioning

confidence: 99%

Applications of Ear Decomposition to Efficient Heterogeneous Algorithms for Shortest Path/Cycle Problems

Dutta

Chaitanya

Kothapalli

et al. 2018

IJNC

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Graph algorithms play an important role in several fields of sciences and engineering. Prominent among them are the All-Pairs-Shortest-Paths (APSP) and related problems. Indeed there are several efficient implementations for such problems on a variety of modern multi-and manycore architectures.It can be noticed that for several graph problems, parallelism offers only a limited success as current parallel architectures have severe short-comings when deployed for most graph algorithms. At the same time, some of these graphs exhibit clear structural properties due to their sparsity. This calls for particular solution strategies aimed at scalable processing of large, sparse graphs on modern parallel architectures.In this paper, we study the applicability of an ear decomposition of graphs to problems such as all-pairs-shortest-paths and minimum cost cycle basis. Through experimentation, we show that the resulting solutions are scalable in terms of both memory usage and also their speedup over best known current implementations. We believe that our techniques have the potential to be relevant for designing scalable solutions for other computations on large sparse graphs.

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Section: Related Workmentioning

confidence: 99%

Applications of Ear Decomposition to Efficient Heterogeneous Algorithms for Shortest Path/Cycle Problems

Dutta

Chaitanya

Kothapalli

et al. 2018

IJNC

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“…As far as we know, there is no efficient parallel algorithm of the SSSP in a SIMD model. Harish and Narayanan [20] proposed using CUDA to accelerate large graph algorithms (including SSSP) on the GPU, however they implemented only a very basic version and did not gain much performance improvement. We use a similar implementation, but take advantage of a newer version of CUDA hardware which supports atomic read/write operations in the device global memory.…”

Section: B Cuda Implementationmentioning

confidence: 99%

GPU accelerated fuzzy connected image segmentation by using CUDA

Zhuge¹,

Cao²,

Miller³

2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Image segmentation techniques using fuzzy connectedness principles have shown their effectiveness in segmenting a variety of objects in several large applications in recent years. However, one problem of these algorithms has been their excessive computational requirements when processing large image datasets. Nowadays commodity graphics hardware provides high parallel computing power. In this paper, we present a parallel fuzzy connected image segmentation algorithm implementation on Nvidia's Compute Unified Device Architecture (CUDA) platform for segmenting large medical image data sets. Our experiments based on three data sets of small, medium, and large data size demonstrate the efficiency of the parallel algorithm, which achieves a speed-up factor of 7.2x, 7.3x, and 14.4x, correspondingly, for the three data sets over the implementation of the algorithm on CPU.

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“…For network-related problems, researchers have devoted efforts to conquering irregular graph structures using GPGPU techniques and have achieved higher performance than is possible with traditional sequential CPU algorithms (Mitchell & Frank, 2017;Merrill, Garland & Grimshaw, 2015;Wang et al, 2015;Harish & Narayanan, 2007;Cong & Bader, 2005). CUDA, developed by Nvidia Corporation, is the most popular GPU computing framework, and some researchers have even used this framework to parallelize the Brandes algorithm (Shi & Zhang, 2011;Sariyüce et al, 2013;McLaughlin & Bader, 2014.…”

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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Accelerating Large Graph Algorithms on the GPU Using CUDA

Cited by 541 publications

References 10 publications

Applications of Ear Decomposition to Efficient Heterogeneous Algorithms for Shortest Path/Cycle Problems

Applications of Ear Decomposition to Efficient Heterogeneous Algorithms for Shortest Path/Cycle Problems

GPU accelerated fuzzy connected image segmentation by using CUDA

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

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