text ^: Efficient Structural Graph Clustering on GPUs

Structural clustering is one of the most popular graph clustering methods, which has achieved great performance improvement by utilizing GPUs. Even though, the state-of-the-art GPU-based structural clustering algorithm, <inline-formula><tex-math notation="LaTeX">\mathsf {GPUSCA...

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Veröffentlicht in:IEEE transactions on parallel and distributed systems Jg. 36; H. 9; S. 1890 - 1903
Hauptverfasser: Yuan, Long, Zhou, Zeyu, Chen, Zi, Lin, Xuemin, Zhao, Xiang, Zhang, Fan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.09.2025
Schlagworte:
Artificial intelligence
Clustering algorithms
Costs
Data mining
GPU
graph algorithm
Graphics processing units
Hardware
Memory management
Partitioning algorithms
Servers
Structural graph clustering
Training
ISSN:1045-9219, 1558-2183
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Zusammenfassung:Structural clustering is one of the most popular graph clustering methods, which has achieved great performance improvement by utilizing GPUs. Even though, the state-of-the-art GPU-based structural clustering algorithm, <inline-formula><tex-math notation="LaTeX">\mathsf {GPUSCAN}</tex-math></inline-formula>, still suffers from efficiency issues since lots of extra costs are introduced for parallelization. Moreover, <inline-formula><tex-math notation="LaTeX">\mathsf {GPUSCAN}</tex-math></inline-formula> assumes that the graph is resident in the GPU memory. However, the GPU memory capacity is limited currently while many real-world graphs are big and cannot fit in the GPU memory, which makes <inline-formula><tex-math notation="LaTeX">\mathsf {GPUSCAN}</tex-math></inline-formula> unable to handle large graphs. Motivated by this, we present a new GPU-based structural clustering algorithm, <inline-formula><tex-math notation="LaTeX">{\mathsf {GPUSCAN^{++}}}</tex-math></inline-formula>, in this paper. To address the efficiency issue, we propose a new progressive clustering method tailored for GPUs that not only avoid high parallelization costs but also fully exploits the computing resources of GPUs. To address the GPU memory limitation issue, we propose a partition-based algorithm for structural clustering that can process large graphs with limited GPU memory. We conduct experiments on real graphs, and the experimental results demonstrate that our algorithm can achieve up to 168 times speedup compared with the state-of-the-art GPU-based algorithm when the graph can be resident in the GPU memory. Moreover, our algorithm is scalable to handle large graphs. As an example, our algorithm can finish the structural clustering on a graph with 1.8 billion edges using less than 2 GB GPU memory.
ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2025.3582996