Enumeration of Billions of Maximal Bicliques in Bipartite Graphs without Using GPUs

Maximal biclique enumeration (MBE) is crucial in bipartite graph analysis. Recent studies rely on extensive set intersections on static bipartite graphs to solve the MBE problem. However, the computational subgraphs dynamically change during enumeration, leading to redundant memory accesses and degr...

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Bibliographic Details
Published in:	SC24: International Conference for High Performance Computing, Networking, Storage and Analysis pp. 1 - 15
Main Authors:	Pan, Zhe, He, Shuibing, Li, Xu, Zhang, Xuechen, Yin, Yanlong, Wang, Rui, Shou, Lidan, Song, Mingli, Sun, Xian-He, Chen, Gang
Format:	Conference Proceeding
Language:	English
Published:	IEEE 17.11.2024
Subjects:	Bipartite graph bitmap Computational efficiency Graphics processing units Heuristic algorithms High performance computing maximal biclique enumeration Memory management
Online Access:	Get full text
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Summary:	Maximal biclique enumeration (MBE) is crucial in bipartite graph analysis. Recent studies rely on extensive set intersections on static bipartite graphs to solve the MBE problem. However, the computational subgraphs dynamically change during enumeration, leading to redundant memory accesses and degraded set intersection performance. To overcome this limitation, we propose an AdaMBE algorithm. First, we redesign its core operations using local neighborhood information derived from computational subgraphs to minimize redundant memory accesses. Second, we dynamically create computational subgraphs using bitmaps leveraging its fast bitwise operations to accelerate set intersections. Finally, we integrate them in AdaMBE. Our experimental results show that AdaMBE is 1.6 \times-49.7 \times faster than its closest CPU-based competitor and successfully enumerates all 19 billion maximal bicliques on the TVTropes dataset, a large task beyond the capabilities of existing algorithms. Notably, on certain datasets, our parallel version, ParAdaMBE, on CPUs even outperforms GMBE on GPUs by up to 5.07 \times.
DOI:	10.1109/SC41406.2024.00106