Yaqi Xia (夏亚奇)

Wuhan University PhD, School of Computer Science, Wuhan University

I am Yaqi Xia, I am currently working toward the Ph.D. degree in computer science with Wuhan University under the supervision of Prof. Dazhao Cheng. My research interests include distributed deep learning model training and high-performance computing system for AI/ML. Before pursuing my Ph.D., I obtained both my Bachelor's and Master's degrees from Xidian University, where I had the privilege of being mentored by Prof. Rui Song.

Google Scholar ORCID
Education
  • Wuhan University

    Wuhan University

    Ph.D. in Artificial Intelligence Sep. 2021 -

  • Xidian University

    Xidian University

    M.S. in Electronics and Communication Engineering Sep. 2018 - Jul. 2021

  • Xidian University

    Xidian University

    B.S. in Communication Engineering Sep. 2014 - Jul. 2018

Honors & Awards
  • Best Paper Runner-up of ACM HPDC23 2023
  • Second Prize of First 'Tianzhi Cup' Artificial Intelligence Challenge 2019
Experience
  • Research Center for Graph Computing, Zhejiang Lab

    Research Center for Graph Computing, Zhejiang Lab

    Research Intern Aug. 2023 - Dec. 2023

News!
  • I am looking for highly self-motivated Bachelor, Master and PhD students. Feel free to get in touch with me via email If you're interested in collaborating.
News
2025
I am looking for highly self-motivated Bachelor, Master and PhD students. Feel free to get in touch with me via email If you're interested in collaborating.
Feb 14
2024
Our work 'Harnessing Inter-GPU Shared Memory for Seamless MoE Communication-Computation Fusion' is accepted by PPoPP 2025.
Dec 12
Our work 'Redundancy-Free and Load-Balanced TGNN Training With Hierarchical Pipeline Parallelism' is accepted by TPDS 2024.
Nov 11
Our work 'Scaling New Heights :Transformative Cross-GPU Sampling for Training Billion-Edge Graphs' is accepted by SC 2024.
May 16
Our work 'Accelerating Distributed DLRM Training with Optimized TT Decomposition and Micro-Batching' is accepted by SC 2024.
Apr 16
Our work 'Raptor-T :A Fused and Memory-Efficient Sparse Transformer for Long and Variable-Length Sequences' is accepted by TC 2024.
Apr 16
Our work 'MPMoE :Memory Efficient MoE for Pre-Trained Models With Adaptive Pipeline Parallelism' is accepted by TPDS 2024.
Apr 08
2023
Our work 'Redundancy-Free High-Performance Dynamic GNN Training with Hierarchical Pipeline Parallelism' is accepted by HPDC 2023 and be selected as Best Paper Nomination (only two nominations)!
Aug 07
Our work 'MPipeMoE: Memory Efficient MoE for Pre-trained Models with Adaptive Pipeline Parallelism' is accepted by IPDPS 2023.
Jul 18
Selected Publications (view all )
Redundancy-free and load-balanced TGNN training with hierarchical pipeline parallelism
Redundancy-free and load-balanced TGNN training with hierarchical pipeline parallelism

Yaqi Xia, Zheng Zhang, Donglin Yang, Chuang Hu, Xiaobo Zhou, Hongyang Chen, Qianlong Sang†, Dazhao Cheng†(† corresponding author)

IEEE Transactions on Parallel and Distributed (TPDS) 2024 JournalCCF-A

This work introduces Sven, a co-designed algorithm-system library aimed at accelerating TGNN training on a multi-GPU platform.

[Paper] [Cite]

Redundancy-free and load-balanced TGNN training with hierarchical pipeline parallelism
Redundancy-free and load-balanced TGNN training with hierarchical pipeline parallelism

Yaqi Xia, Zheng Zhang, Donglin Yang, Chuang Hu, Xiaobo Zhou, Hongyang Chen, Qianlong Sang†, Dazhao Cheng†(† corresponding author)

IEEE Transactions on Parallel and Distributed (TPDS) 2024 JournalCCF-A

This work introduces Sven, a co-designed algorithm-system library aimed at accelerating TGNN training on a multi-GPU platform.

[Paper] [Cite]

Scaling New Heights :Transformative Cross-GPU Sampling for Training Billion-Edge Graphs
Scaling New Heights :Transformative Cross-GPU Sampling for Training Billion-Edge Graphs

Yaqi Xia, Donglin Yang, Xiaobo Zhou, Dazhao Cheng†(† corresponding author)

The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC) 2024 ConferenceCCF-A

In this paper, we introduced HyDRA, a pioneering framework for sampling-based GNN training on large-scale graphs.

[Paper]

Scaling New Heights :Transformative Cross-GPU Sampling for Training Billion-Edge Graphs
Scaling New Heights :Transformative Cross-GPU Sampling for Training Billion-Edge Graphs

Yaqi Xia, Donglin Yang, Xiaobo Zhou, Dazhao Cheng†(† corresponding author)

The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC) 2024 ConferenceCCF-A

In this paper, we introduced HyDRA, a pioneering framework for sampling-based GNN training on large-scale graphs.

[Paper]

Redundancy-Free High-Performance Dynamic GNN Training with Hierarchical Pipeline Parallelism
Redundancy-Free High-Performance Dynamic GNN Training with Hierarchical Pipeline Parallelism

Yaqi Xia, Zheng Zhang, Hulin Wang, Donglin Yang, Xiaobo Zhou, Dazhao Cheng†(† corresponding author)

The 32nd International Symposium on High-Performance Parallel and Distributed Computing (ACM HPDC) 2023 ConferenceCCF-BBest Paper Nomination

This paper presents Sven, an algorithm and system co-designed TGNN training library for the end-to-end performance optimization on multi-node multi-GPU systems.

[Paper] [Cite]

Redundancy-Free High-Performance Dynamic GNN Training with Hierarchical Pipeline Parallelism
Redundancy-Free High-Performance Dynamic GNN Training with Hierarchical Pipeline Parallelism

Yaqi Xia, Zheng Zhang, Hulin Wang, Donglin Yang, Xiaobo Zhou, Dazhao Cheng†(† corresponding author)

The 32nd International Symposium on High-Performance Parallel and Distributed Computing (ACM HPDC) 2023 ConferenceCCF-BBest Paper Nomination

This paper presents Sven, an algorithm and system co-designed TGNN training library for the end-to-end performance optimization on multi-node multi-GPU systems.

[Paper] [Cite]

ASFM-Net :Asymmetrical Siamese Feature Matching Network for Point Completion
ASFM-Net :Asymmetrical Siamese Feature Matching Network for Point Completion

Yaqi Xia*, Yan Xia*, Wei Li, Rui Song†, Kailang Cao, Uwe Stilla(† corresponding author)

Proceedings of the 29th ACM international conference on multimedia (ACM MM) 2021 ConferenceCCF-A

We tackle the problem of object completion from point clouds and propose a novel point cloud completion network employing an Asymmetrical Siamese Feature Matching strategy, termed as ASFM-Net.

[Paper] [Cite]

ASFM-Net :Asymmetrical Siamese Feature Matching Network for Point Completion
ASFM-Net :Asymmetrical Siamese Feature Matching Network for Point Completion

Yaqi Xia*, Yan Xia*, Wei Li, Rui Song†, Kailang Cao, Uwe Stilla(† corresponding author)

Proceedings of the 29th ACM international conference on multimedia (ACM MM) 2021 ConferenceCCF-A

We tackle the problem of object completion from point clouds and propose a novel point cloud completion network employing an Asymmetrical Siamese Feature Matching strategy, termed as ASFM-Net.

[Paper] [Cite]

All publications