Exploiting Reuse for GPU Subgraph Enumeration

doi:10.1109/TKDE.2020.3035564

Exploiting Reuse for GPU Subgraph Enumeration

2022-09-01

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DOI：10.1109/TKDE.2020.3035564

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Subgraph enumeration is important for many applications such as network motif discovery, community detection, and frequent subgraph mining. To accelerate the execution, recent works utilize graphics processing units (GPUs) to parallelize subgraph enumeration. The performances of these parallel schemes are dominated by the set intersection operations which account for up to 95 percent of the total processing time. (Un)surprisingly, a significant portion (as high as 99 percent) of these operations is actually redundant, i.e., the same set of vertices is repeatedly encountered and evaluated. Therefore, in this article, we seek to salvage and recycle the results of such operations to avoid repeated computation. Our solution consists of two phases. In the first phase, we generate a reusable plan that determines the opportunity for reuse. The plan is based on a novel reuse discovery mechanism that can identify available results to prevent redundant computation. In the second phase, the plan is executed to produce the subgraph enumeration results. This processing is based on a newly designed reusable parallel search strategy that can efficiently maintain and retrieve the results of set intersection operations. Our implementation on GPUs shows that our approach can achieve up to 5 times speedups compared with the state-of-the-art GPU solutions.

Keywords:

Graphics processing units

Acceleration

Pattern matching

Data structures

Instruction sets

Runtime

Data mining

Subgraph enumeration

GPU

reuse

Exploiting Reuse for GPU Subgraph Enumeration

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