LearnGraph: A Learning-Based Architecture for Dynamic Graph Processing

Dynamic graph processing systems using conventional array-based architectures face significant throughput limitations due to inefficient memory access and index management. While learned indexes improve data structure access, they struggle with interconnected graph data. We present LearnGraph, a nov...

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Veröffentlicht in:2025 62nd ACM/IEEE Design Automation Conference (DAC) S. 1 - 7
Hauptverfasser: Zhang, Lingling, Wu, Yijian, Jiang, Hong, Zhou, Ziyu, Lu, Tiancheng
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 22.06.2025
Schlagworte:
Adaptation models
Data models
Indexes
Organizations
Performance gain
Predictive models
Standards organizations
Systems architecture
Throughput
Topology
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Abstract Dynamic graph processing systems using conventional array-based architectures face significant throughput limitations due to inefficient memory access and index management. While learned indexes improve data structure access, they struggle with interconnected graph data. We present LearnGraph, a novel architecture with an adaptive tree-based memory manager that dynamically optimizes for graph topology and access patterns. Our design integrates two key components: a hierarchical learned index optimized for graph topology to predict vertex and edge locations, and an adaptive tree structure that automatically reorganizes memory regions based on access patterns. Evaluation results demonstrate that LearnGraph outperforms state-of-theart dynamic graph systems, achieving 3.4 \times higher throughput on average and reducing processing time by 1.7 \times to 11 \times across standard graph workloads.
AbstractList Dynamic graph processing systems using conventional array-based architectures face significant throughput limitations due to inefficient memory access and index management. While learned indexes improve data structure access, they struggle with interconnected graph data. We present LearnGraph, a novel architecture with an adaptive tree-based memory manager that dynamically optimizes for graph topology and access patterns. Our design integrates two key components: a hierarchical learned index optimized for graph topology to predict vertex and edge locations, and an adaptive tree structure that automatically reorganizes memory regions based on access patterns. Evaluation results demonstrate that LearnGraph outperforms state-of-theart dynamic graph systems, achieving 3.4 \times higher throughput on average and reducing processing time by 1.7 \times to 11 \times across standard graph workloads.
Author Jiang, Hong
Lu, Tiancheng
Wu, Yijian
Zhou, Ziyu
Zhang, Lingling
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  givenname: Tiancheng
  surname: Lu
  fullname: Lu, Tiancheng
  organization: Capital Normal University
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SubjectTerms Adaptation models
Data models
Indexes
Organizations
Performance gain
Predictive models
Standards organizations
Systems architecture
Throughput
Topology
Title LearnGraph: A Learning-Based Architecture for Dynamic Graph Processing
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