Parallel $k$-Core Decomposition: Theory and Practice
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866910889357082624 |
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| author | Liu, Youzhe Dong, Xiaojun Gu, Yan Sun, Yihan |
| author_facet | Liu, Youzhe Dong, Xiaojun Gu, Yan Sun, Yihan |
| contents | This paper proposes efficient solutions for $k$-core decomposition with high parallelism. The problem of $k$-core decomposition is fundamental in graph analysis and has applications across various domains. However, existing algorithms face significant challenges in achieving work-efficiency in theory and/or high parallelism in practice, and suffer from various performance bottlenecks.
We present a simple, work-efficient parallel framework for $k$-core decomposition that is easy to implement and adaptable to various strategies for improving work-efficiency. We introduce two techniques to enhance parallelism: a sampling scheme to reduce contention on high-degree vertices, and vertical granularity control (VGC) to mitigate scheduling overhead for low-degree vertices. Furthermore, we design a hierarchical bucket structure to optimize performance for graphs with high coreness values.
We evaluate our algorithm on a diverse set of real-world and synthetic graphs. Compared to state-of-the-art parallel algorithms, including ParK, PKC, and Julienne, our approach demonstrates superior performance on 23 out of 25 graphs when tested on a 96-core machine. Our algorithm shows speedups of up to 315$\times$ over ParK, 33.4$\times$ over PKC, and 52.5$\times$ over Julienne. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_08042 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Parallel $k$-Core Decomposition: Theory and Practice Liu, Youzhe Dong, Xiaojun Gu, Yan Sun, Yihan Data Structures and Algorithms Distributed, Parallel, and Cluster Computing This paper proposes efficient solutions for $k$-core decomposition with high parallelism. The problem of $k$-core decomposition is fundamental in graph analysis and has applications across various domains. However, existing algorithms face significant challenges in achieving work-efficiency in theory and/or high parallelism in practice, and suffer from various performance bottlenecks. We present a simple, work-efficient parallel framework for $k$-core decomposition that is easy to implement and adaptable to various strategies for improving work-efficiency. We introduce two techniques to enhance parallelism: a sampling scheme to reduce contention on high-degree vertices, and vertical granularity control (VGC) to mitigate scheduling overhead for low-degree vertices. Furthermore, we design a hierarchical bucket structure to optimize performance for graphs with high coreness values. We evaluate our algorithm on a diverse set of real-world and synthetic graphs. Compared to state-of-the-art parallel algorithms, including ParK, PKC, and Julienne, our approach demonstrates superior performance on 23 out of 25 graphs when tested on a 96-core machine. Our algorithm shows speedups of up to 315$\times$ over ParK, 33.4$\times$ over PKC, and 52.5$\times$ over Julienne. |
| title | Parallel $k$-Core Decomposition: Theory and Practice |
| topic | Data Structures and Algorithms Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2502.08042 |