ACGraph: Accelerating Streaming Graph Processing via Dependence Hierarchy

Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of processing vertices influenced by updates. To address this issue, we propose ACGraph, a novel streaming graph processing approach for monoton...

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Veröffentlicht in:2023 60th ACM/IEEE Design Automation Conference (DAC) S. 1 - 6
Hauptverfasser: Jiang, Zihan, Mao, Fubing, Guo, Yapu, Liu, Xu, Liu, Haikun, Liao, Xiaofei, Jin, Hai, Zhang, Wei
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 09.07.2023
Schlagworte:
Design automation
graph processing
incremental computation
Runtime
state propagation
streaming graph
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Abstract Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of processing vertices influenced by updates. To address this issue, we propose ACGraph, a novel streaming graph processing approach for monotonic graph algorithms. It maintains dependence trees during runtime, and makes affected vertices processed in a top-to-bottom order in the hierarchy of the dependence trees, thus normalizing the state propagation order and coalescing of multiple propagation to the same vertices. Experimental results show that ACGraph reduces the number of updates by 50% on average, and achieves the speedup of 1.75~7.43× over state-of-the-art systems.
AbstractList Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of processing vertices influenced by updates. To address this issue, we propose ACGraph, a novel streaming graph processing approach for monotonic graph algorithms. It maintains dependence trees during runtime, and makes affected vertices processed in a top-to-bottom order in the hierarchy of the dependence trees, thus normalizing the state propagation order and coalescing of multiple propagation to the same vertices. Experimental results show that ACGraph reduces the number of updates by 50% on average, and achieves the speedup of 1.75~7.43× over state-of-the-art systems.
Author Liu, Xu
Zhang, Wei
Mao, Fubing
Guo, Yapu
Jiang, Zihan
Liu, Haikun
Liao, Xiaofei
Jin, Hai
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  givenname: Xiaofei
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  givenname: Wei
  surname: Zhang
  fullname: Zhang, Wei
  email: wei.zhang@ust.hk
  organization: HKUST,Department of Electronic and Computer Engineering,Hong Kong
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Snippet Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of...
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SubjectTerms Design automation
graph processing
incremental computation
Runtime
state propagation
streaming graph
Title ACGraph: Accelerating Streaming Graph Processing via Dependence Hierarchy
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