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SMDG: Enhancing In-Memory Dynamic Graph Processing With Storage-Class Memory

delete2026-03-23
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PRE
AI
T
Tongfeng Weng
M
Mo Sha
周旭 cover
周旭 (Xu Zhou)
J
Jingjing Lu
W
Wentao Huang
K
K. L. Li
K
Kian‐Lee Tan
DOI:10.1109/TKDE.2026.3676514delete
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Abstract

Abstract

En 中文
In-memory dynamic graph processing faces three critical challenges: limited DRAM capacity, inefficient concurrent update/query handling, and vulnerability to crashes. Traditional segment-level systems struggle with write amplification on emerging Storage-Class Memory (SCM), while existing persistent-memory systems suffer from coarse-grained synchronization and high recovery overhead. This study presents the Storage-Class Memory Dynamic Graph (SMDG) processing framework, an architecture-level redesign centered on the block as the atomic unit across storage, concurrency, and recovery. The system addresses these challenges through three key innovations. First, a block-granular storage design organizes adjacency data at fixed-size block granularity on heterogeneous DRAM-SCM architecture, employing buffered batched writes to significantly reduce write amplification while preserving logarithmic update complexity. Second, block-level multi-version concurrency control maintains timestamped block versions under per-vertex read-write synchronization to provide task-ordered snapshot visibility for concurrent queries without copying entire vertices or pages. Third, a block-granular crash recovery protocol with decentralized per-vertex logs enables independent parallel reconstruction, ensuring application-level semantic consistency while achieving substantially faster recovery than sequential approaches. Experimental results validate that this unified block-granular design improves update efficiency, sustains mixed update-query workloads with controlled memory overhead, and accelerates crash recovery compared with prior dynamic graph systems.
Keywords:
Dynamic graph
MVCC
packed-memory array
storage-class memory

Journal

IEEE Transactions on Knowledge and Data Engineering cover
IEEE Transactions on Knowledge and Data Engineering
IF:
10.4
Papers:
6.9K
Citations:
3.2W

Organization

A
alibaba cloud
Scholars:
39
Papers: 10
Citations: 1
H
hunan university
Scholars:
4.5W
Papers: 3.3W
Citations: 70
N
National University of Singapore
Scholars:
7.6W
Papers: 6.5W
Citations: 11.4W
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Cited Papers

Cited Papers

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