Computing Strongly Connected Components in Parallel on CUDA

Barnat, Jǐŕı; Bauch, Petr; Brim, Luboš; Ceka, Milan

doi:10.1109/ipdps.2011.59

Cited by 80 publications

(61 citation statements)

References 18 publications

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“…For SCC decomposition, this is a significant improvement over previous results (e.g. [1]) for sparse graphs with a low average out-degree.…”

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confidence: 54%

“…The high relevance of SCCs has led to various dedicated variants of Tarjan's classical algorithm [30] such as a symbolic variant [9] and a plethora of parallel algorithms [1,23,26]. In the context of probabilistic model checking, a generalisation of SCCs-known as maximal end components (MECs)-play a pivotal role [2,17].…”

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confidence: 99%

“…First, inspired by the Forward-Backward algorithm (FB) [20], each thread combines a forward and a backward reachability search so as to identify SCCs. Previous work on GPU-based SCC decomposition [1,23,26] identified the FB algorithm (combined with a trimming procedure to remove trivial SCCs) as the best performing one for general input graphs. Opposed to these works, we focus on graphs that are commonly observed in model checking, i.e., sparse graphs with a low average out-degree (number of outgoing transitions per state) and tailor our algorithms to treat these graphs efficiently.…”

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confidence: 99%

“…The overall memory requirements are significantly lower than for competitive algorithms [1] as besides the input graph G = (V, E), only a single additional integer array of size |V | is needed to store decomposition results. Given the restricted memory size on a GPU, this memory reduction is essential.…”

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confidence: 99%

“…To the best of our knowledge, this is the first GPU-based MEC decomposition procedure. We implemented our algorithms using CUDA 1 for NVIDIA GPUs, and ran them on examples of the PRISM benchmark suite [25]. Speed-up factors of 15-30 and 79 have been achieved for SCC and MEC decomposition, respectively.…”

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confidence: 99%

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Efficient GPU algorithms for parallel decomposition of graphs into strongly connected and maximal end components

Wijs

Katoen

BošnaăźKi

2016

Form Methods Syst Des

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This article presents parallel algorithms for component decomposition of graph structures on general purpose graphics processing units (GPUs). In particular, we consider the problem of decomposing sparse graphs into strongly connected components, and decomposing graphs induced by stochastic games (such as Markov decision processes) into maximal end components. These problems are key ingredients of many (probabilistic) model-checking algorithms. We explain the main rationales behind our GPU-algorithms, and show a significant speed-up over the sequential (as well as existing parallel) counterparts in several case studies.

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“…For SCC decomposition, this is a significant improvement over previous results (e.g. [1]) for sparse graphs with a low average out-degree.…”

mentioning

confidence: 54%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Efficient GPU algorithms for parallel decomposition of graphs into strongly connected and maximal end components

Wijs

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BošnaăźKi

2016

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Efficient and high‐quality sparse graph coloring on GPUs

Chen

Fang

et al. 2016

Concurrency and Computation

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Summary Graph coloring has been broadly used to discover concurrency in parallel computing. To speed up graph coloring for large‐scale datasets, parallel algorithms have been proposed to leverage modern GPUs. Existing GPU implementations either have limited performance or yield unsatisfactory coloring quality (too many colors assigned). We present a work‐efficient parallel graph coloring implementation on GPUs with good coloring quality. Our approach uses the speculative greedy scheme, which inherently yields better quality than the method of finding maximal independent set. To achieve high performance on GPUs, we refine the algorithm to leverage efficient operators and alleviate conflicts. We also incorporate common optimization techniques to further improve performance. Our method is evaluated with both synthetic and real‐world sparse graphs on the NVIDIA GPU. Experimental results show that our proposed implementation achieves averaged 4.1 × (up to 8.9 × ) speedup over the serial implementation. It also outperforms the existing GPU implementation from the NVIDIA CUSPARSE library (2.2 × average speedup), while yielding much better coloring quality than CUSPARSE.

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A task‐based approach for finding SCCs in real‐world graphs on external memory

Shao

et al. 2017

Concurrency and Computation

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Summary Finding strongly connected components (SCCs) in graphs is one of the important research topics of graph data mining. Traditional SCC‐finding methods need to load the whole graph into main memory before actual processing, which makes them inappropriate to process today's large graphs. Although it is cost‐effective to conductive SCC‐finding in the external memory (EM) graph processing systems, existing EM systems are still inefficient on such workload for 2 reasons: data‐parallel processing model and inefficient graph mutation mechanism. In this paper, we propose a task‐based approach, named as TAS, for finding SCCs in large real‐world graphs. TAS encapsulates individual graph dataset and the SCC‐finding algorithms conducted on it as an independent task. By this strategy, different algorithms can be assigned to different datasets according to the stage of processing or the size of the dataset. By gradually removing unneeded data, ie, the known SCCs, with an efficient graph mutation method, TAS continuously reduces the scale of the problem to accelerate processing. Performance evaluation on large real‐world graphs show that TAS greatly outperforms existing EM solutions on finding SCCs (eg, 55.2x faster than GraphChi and 40.3x faster than X‐Stream).

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Computing Strongly Connected Components in Parallel on CUDA

Cited by 80 publications

References 18 publications

Efficient GPU algorithms for parallel decomposition of graphs into strongly connected and maximal end components

Efficient GPU algorithms for parallel decomposition of graphs into strongly connected and maximal end components

Efficient and high‐quality sparse graph coloring on GPUs

A task‐based approach for finding SCCs in real‐world graphs on external memory

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