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HSampler : Optimizing Multi-GPU GNN Sampling with Collision-Avoid Selection

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Yuyang Jin
Jidong Zhai
Kezhao Huang
Weimin Zheng
Published in
Network and Parallel Computing
Publisher
Springer Nature Switzerland

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Abstract

This chapter explores the challenges and optimizations in multi-GPU Graph Neural Network (GNN) sampling, focusing on the performance bottlenecks in the sampling stage. It introduces HSampler, a system designed to optimize GNN training by reducing selection collision overhead through probability reordering and sliding selection techniques. The text delves into the background of GNNs, the inefficiencies in biased sampling, and the innovative solutions proposed by HSampler. It also discusses the implementation of an adaptive asynchronous execution model to overlap sampling with training, further enhancing performance. The evaluation section presents a comprehensive performance analysis, comparing HSampler with existing systems like DGL on large-scale datasets. The results demonstrate significant improvements in both sampling and end-to-end training performance, making HSampler a promising solution for efficient large-scale graph training.
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next chapter ServScale: Concurrency-Aware Serverless Execution and Scaling Paradigm
Title
HSampler : Optimizing Multi-GPU GNN Sampling with Collision-Avoid Selection
Authors
Yuyang Jin
Jidong Zhai
Kezhao Huang
Weimin Zheng
Copyright Year
2026
Book
Network and Parallel Computing

Print ISBN: 978-3-032-10458-8

Electronic ISBN: 978-3-032-10459-5

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-10459-5_1
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