InferTurbo: A Scalable System for Boosting Full-graph Inference of Graph Neural Network over Huge Graphs Article Swipe
Dalong Zhang
,
Xianzheng Song
,
Zhiyang Hu
,
Yang Li
,
Tao Miao
,
Binbin Hu
,
Lin Wang
,
Zhiqiang Zhang
,
Jun Zhou
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.00228
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.00228
GNN inference is a non-trivial task, especially in industrial scenarios with giant graphs, given three main challenges, i.e., scalability tailored for full-graph inference on huge graphs, inconsistency caused by stochastic acceleration strategies (e.g., sampling), and the serious redundant computation issue. To address the above challenges, we propose a scalable system named InferTurbo to boost the GNN inference tasks in industrial scenarios. Inspired by the philosophy of ``think-like-a-vertex", a GAS-like (Gather-Apply-Scatter) schema is proposed to describe the computation paradigm and data flow of GNN inference. The computation of GNNs is expressed in an iteration manner, in which a vertex would gather messages via in-edges and update its state information by forwarding an associated layer of GNNs with those messages and then send the updated information to other vertexes via out-edges. Following the schema, the proposed InferTurbo can be built with alternative backends (e.g., batch processing system or graph computing system). Moreover, InferTurbo introduces several strategies like shadow-nodes and partial-gather to handle nodes with large degrees for better load balancing. With InferTurbo, GNN inference can be hierarchically conducted over the full graph without sampling and redundant computation. Experimental results demonstrate that our system is robust and efficient for inference tasks over graphs containing some hub nodes with many adjacent edges. Meanwhile, the system gains a remarkable performance compared with the traditional inference pipeline, and it can finish a GNN inference task over a graph with tens of billions of nodes and hundreds of billions of edges within 2 hours.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2307.00228
- https://arxiv.org/pdf/2307.00228
- OA Status
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- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4383175891Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2307.00228Digital Object Identifier
- Title
-
InferTurbo: A Scalable System for Boosting Full-graph Inference of Graph Neural Network over Huge GraphsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-07-01Full publication date if available
- Authors
-
Dalong Zhang, Xianzheng Song, Zhiyang Hu, Yang Li, Tao Miao, Binbin Hu, Lin Wang, Zhiqiang Zhang, Jun ZhouList of authors in order
- Landing page
-
https://arxiv.org/abs/2307.00228Publisher landing page
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https://arxiv.org/pdf/2307.00228Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2307.00228Direct OA link when available
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Computer science, Inference, Scalability, Computation, Theoretical computer science, Graph, Inference engine, Vertex (graph theory), Algorithm, Artificial intelligence, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.messages | 100, 117 |
| abstract_inverted_index.paradigm | 77 |
| abstract_inverted_index.proposed | 72, 133 |
| abstract_inverted_index.sampling | 181 |
| abstract_inverted_index.scalable | 48 |
| abstract_inverted_index.system). | 148 |
| abstract_inverted_index.tailored | 19 |
| abstract_inverted_index.vertexes | 126 |
| abstract_inverted_index.Following | 129 |
| abstract_inverted_index.Moreover, | 149 |
| abstract_inverted_index.computing | 147 |
| abstract_inverted_index.conducted | 175 |
| abstract_inverted_index.efficient | 194 |
| abstract_inverted_index.expressed | 89 |
| abstract_inverted_index.inference | 1, 22, 56, 171, 196, 219, 227 |
| abstract_inverted_index.iteration | 92 |
| abstract_inverted_index.pipeline, | 220 |
| abstract_inverted_index.redundant | 37, 183 |
| abstract_inverted_index.scenarios | 9 |
| abstract_inverted_index.InferTurbo | 51, 134, 150 |
| abstract_inverted_index.Meanwhile, | 208 |
| abstract_inverted_index.associated | 111 |
| abstract_inverted_index.balancing. | 167 |
| abstract_inverted_index.containing | 200 |
| abstract_inverted_index.especially | 6 |
| abstract_inverted_index.forwarding | 109 |
| abstract_inverted_index.full-graph | 21 |
| abstract_inverted_index.industrial | 8, 59 |
| abstract_inverted_index.inference. | 83 |
| abstract_inverted_index.introduces | 151 |
| abstract_inverted_index.out-edges. | 128 |
| abstract_inverted_index.philosophy | 64 |
| abstract_inverted_index.processing | 143 |
| abstract_inverted_index.remarkable | 213 |
| abstract_inverted_index.sampling), | 33 |
| abstract_inverted_index.scenarios. | 60 |
| abstract_inverted_index.stochastic | 29 |
| abstract_inverted_index.strategies | 31, 153 |
| abstract_inverted_index.InferTurbo, | 169 |
| abstract_inverted_index.alternative | 139 |
| abstract_inverted_index.challenges, | 16, 44 |
| abstract_inverted_index.computation | 38, 76, 85 |
| abstract_inverted_index.demonstrate | 187 |
| abstract_inverted_index.information | 107, 123 |
| abstract_inverted_index.non-trivial | 4 |
| abstract_inverted_index.performance | 214 |
| abstract_inverted_index.scalability | 18 |
| abstract_inverted_index.traditional | 218 |
| abstract_inverted_index.Experimental | 185 |
| abstract_inverted_index.acceleration | 30 |
| abstract_inverted_index.computation. | 184 |
| abstract_inverted_index.shadow-nodes | 155 |
| abstract_inverted_index.inconsistency | 26 |
| abstract_inverted_index.hierarchically | 174 |
| abstract_inverted_index.partial-gather | 157 |
| abstract_inverted_index.(Gather-Apply-Scatter) | 69 |
| abstract_inverted_index.``think-like-a-vertex", | 66 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |