Back to papers
Accelerating Large Scale Real-Time GNN Inference using Channel Pruning
Summary: Channel pruning via LASSO identifies influential GNN channels per layer for large-scale real-time inference. Two inference regimes and a feature-reuse scheme cut compute/memory, achieving 3.27x GPU and 6.67x CPU speedups with minimal accuracy loss.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
hc152e2e27e2c5453
Venue
VLDB
Year
2021
Pagerank
8.4652234e-05
Overall Rank
2,439 | 83.61%
DOI
10.14778/3461535.3461547
Incoming Non-self Citations Over Time
BibTeX Citation
Copy BibTeX
@article{zhou_vldb21,
title = {{Accelerating Large Scale Real-Time GNN Inference using Channel Pruning}},
author = {Zhou, Hongkuan and Srivastava, Ajitesh and Zeng, Hanqing and Kannan, Rajgopal and Prasanna, Viktor},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {9},
pages = {1597--1605},
doi = {10.14778/3461535.3461547},
url = {https://doi.org/10.14778/3461535.3461547},
year = {2021}
}
Incoming Citations (Sorted by Pagerank)
Showing 11 of 11 citing papers.
Rank
Citing Paper
Year
Venue
Pagerank
1,134
SANCUS: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks
2022
VLDB
0.00011893521
4,622
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning
2022
VLDB
6.4982876e-05
6,166
Apt-Serve: Adaptive Request Scheduling on Hybrid Cache for Scalable LLM Inference Serving
2025
SIGMOD
5.8631131e-05
9,900
Scalable Graph Convolutional Network Training on Distributed-Memory Systems
2023
VLDB
5.1116429e-05
10,105
View-based Explanations for Graph Neural Networks
2024
SIGMOD
5.0789354e-05
10,520
A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness
2026
SIGMOD
4.9793485e-05
10,609
SG-Serve: Efficient Model Serving for Subgraph-based Graph Representation Learning
2026
SIGMOD
4.9793485e-05
10,706
Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling
2026
VLDB
4.9793485e-05
11,307
Inference-friendly Graph Compression for Graph Neural Networks
2025
VLDB
4.9793485e-05
11,386
Graph Compression for Interpretable Graph Neural Network Inference At Scale
2025
VLDB
4.9793485e-05
11,609
Complex-Path: Effective and Efficient Node Ranking with Paths in Billion-Scale Heterogeneous Graphs
2024
VLDB
4.9793485e-05
Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Rank
Cited Paper
Year
Venue
Pagerank
Semantically Similar Papers
#
Overall Rank
Paper
Year
Venue
1
4,826
Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses
2024
VLDB
2
4,898
Comprehensive Evaluation of GNN Training Systems: A Data Management Perspective
2024
VLDB
3
1,772
ByteGNN: Efficient Graph Neural Network Training at Large Scale
2022
VLDB
4
6,826
SIMPLE: Efficient Temporal Graph Neural Network Training at Scale with Dynamic Data Placement
2024
SIGMOD
5
6,780
Eliminating Data Processing Bottlenecks in GNN Training over Large Graphs via Two-level Feature Compression
2024
VLDB
6
10,520
A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness
2026
SIGMOD
7
5,339
FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training
2024
VLDB
8
6,669
Efficient Training of Graph Neural Networks on Large Graphs
2024
VLDB
9
11,307
Inference-friendly Graph Compression for Graph Neural Networks
2025
VLDB
10
2,596
Scalable and Efficient Full-Graph GNN Training for Large Graphs
2023
SIGMOD