iSpan: Parallel Identification of Strongly Connected Components with Spanning Trees

Ji, Yuede; Liu, Huan; Huang, H. Howie

doi:10.1109/sc.2018.00061

Cited by 22 publications

(25 citation statements)

References 57 publications

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“…4), it can be at most 4× slower than the CC algorithm reported by Dhulipala et al [45]. Overall, it is expected that customized shared-memory codes [33,44,45] would perform better than our algorithms based on general-purpose linear algebra libraries. LACC and FastSV achieve the state-of-the-art performance in distributed memory.…”

Section: Opportunities and Limitations Of Linear-algebraic CC Algorithmsmentioning

confidence: 59%

“…The implementation of Afforest in the GAP benchmark [43] runs up to 5× faster than the shared-memory FastSV code implemented on top of SuiteSparse:GraphBLAS. Similarly, the iSpan algorithm [44] uses various asynchronous schemes and direction-optimized BFS to find connected components quickly and can run faster than our algorithms on multicore processors. Recently, Dhulipala et al [45] class of shared-memory parallel graph algorithms that achieve state-of-the-art performance on multicore servers with large memory.…”

Section: Opportunities and Limitations Of Linear-algebraic CC Algorithmsmentioning

confidence: 99%

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Parallel algorithms for finding connected components using linear algebra

Zhang

Azad

Buluç

2020

Journal of Parallel and Distributed Computing

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Finding connected components is one of the most widely used operations on a graph. Optimal serial algorithms for the problem have been known for half a century, and many competing parallel algorithms have been proposed over the last several decades under various different models of parallel computation. This paper presents a class of parallel connected-component algorithms designed using linear-algebraic primitives. These algorithms are based on a PRAM algorithm by Shiloach and Vishkin and can be designed using standard GraphBLAS operations. We demonstrate two algorithms of this class, one named LACC for Linear Algebraic Connected Components, and the other named FastSV which can be regarded as LACC's simplification. With the support of the highly-scalable Combinatorial BLAS library, LACC and FastSV outperform the previous state-of-the-art algorithm by a factor of up to 12x for small to medium scale graphs. For large graphs with more than 50B edges, LACC and FastSV scale to 4K nodes (262K cores) of a Cray XC40 supercomputer and outperform previous algorithms by a significant margin. This remarkable performance is accomplished by (1) exploiting sparsity that was not present in the original PRAM algorithm formulation, (2) using high-performance primitives of Combinatorial BLAS, and (3) identifying hot spots and optimizing them away by exploiting algorithmic insights.

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Section: Opportunities and Limitations Of Linear-algebraic CC Algorithmsmentioning

confidence: 59%

Section: Opportunities and Limitations Of Linear-algebraic CC Algorithmsmentioning

confidence: 99%

Parallel algorithms for finding connected components using linear algebra

Zhang

Azad

Buluç

2020

Journal of Parallel and Distributed Computing

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“…To the best of our knowledge, there exists little work on parallel trimming over large data graphs, except for [30,29,54,32,11]. In these studies, the graph trimming is adopted to quickly remove the vertices without out-going edges so that can speed up the strongly connected component (SCC) decomposition.…”

Section: Introductionmentioning

confidence: 99%

“…However, it has a quadratic worst-case time complexity of O(nm/P + α), where n is the number of vertices, m is the number of edges, P is the number of worker processes, and α is the depth of the algorithm (explained in the next section). This parallel trimming technique is widely used in later SCC decomposition methods [54,32,11].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Parallel Graph Trimming by Arc-Consistency

Guo¹,

Sekerinski²

2021

Preprint

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Given a large data graph, trimming techniques can reduce the search space by removing vertices without outgoing edges. One application is to speed up the parallel decomposition of graphs into strongly connected components (SCC decomposition), which is a fundamental step for analyzing graphs. We observe that graph trimming is essentially a kind of arc-consistency problem, and AC-3, AC-4, and AC-6 are the most relevant arc-consistency algorithms for application to graph trimming. The existing parallel graph trimming methods require worst-case O(nm) time and worst-case O(n) space for graphs with n vertices and m edges. We call these parallel AC-3-based as they are much like the AC-3 algorithm. In this work, we propose AC-4-based and AC-6-based trimming methods. That is, AC-4-based trimming has an improved worst-case time of O(n + m) but requires worst-case space of O(n + m); compared with AC-4-based trimming, AC-6-based has the same worst-case time of O(n + m) but an improved worst-case space of O(n). We parallelize the AC-4-based and AC-6-based algorithms to be suitable for shared-memory multi-core machines. The algorithms are designed to minimize synchronization overhead. For these algorithms, we also prove the correctness and analyze time complexities with the work-depth model.In experiments, we compare these three parallel trimming algorithms over a variety of real and synthetic graphs. Specifically, for the maximum number of traversed edges per worker by using 16 workers, AC-3-based traverses up to 58.3 and 36.5 times more edges than AC-6-based trimming and AC-4-based trimming, respectively. That is, AC-6-based trimming traverses much fewer edges than other methods, which is meaningful especially for implicit graphs. In particular, for the practical running time, AC-6-based trimming achieves high speedups over graphs with a large portion of trimable vertices.

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Vestige: Identifying Binary Code Provenance for Vulnerability Detection

Cui

Huang

2021

Applied Cryptography and Network Security

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iSpan: Parallel Identification of Strongly Connected Components with Spanning Trees

Cited by 22 publications

References 57 publications

Parallel algorithms for finding connected components using linear algebra

Parallel algorithms for finding connected components using linear algebra

Efficient Parallel Graph Trimming by Arc-Consistency

Vestige: Identifying Binary Code Provenance for Vulnerability Detection

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