Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses

Author:

Park Jeongmin Brian1,Mailthody Vikram Sharma2,Qureshi Zaid2,Hwu Wen-mei3

Affiliation:

1. UIUC, USA

2. NVIDIA, USA

3. NVIDIA/UIUC, USA

Abstract

Graph Neural Networks (GNNs) are emerging as a powerful tool for learning from graph-structured data and performing sophisticated inference tasks in various application domains. Although GNNs have been shown to be effective on modest-sized graphs, training them on large-scale graphs remains a significant challenge due to the lack of efficient storage access and caching methods for graph data. Existing frameworks for training GNNs use CPUs for graph sampling and feature aggregation, while the training and updating of model weights are executed on GPUs. However, our in-depth profiling shows CPUs cannot achieve the graph sampling and feature aggregation throughput required to keep up with GPUs. Furthermore, when the graph and its embeddings do not fit in the CPU memory, the overhead introduced by the operating system, say for handling page-faults, causes gross under-utilization of hardware and prolonged end-to-end execution time. To address these issues, we propose the GPU Initiated Direct Storage Access (GIDS) dataloader, to enable GPU-oriented GNN training for large-scale graphs while efficiently utilizing all hardware resources, such as CPU memory, storage, and GPU memory. The GIDS dataloader first addresses memory capacity constraints by enabling GPU threads to directly fetch feature vectors from storage. Then, we introduce a set of innovative solutions, including the dynamic storage access accumulator, constant CPU buffer, and GPU software cache with window buffering, to balance resource utilization across the entire system for improved end-to-end training throughput. Our evaluation using a single GPU on terabyte-scale GNN datasets shows that the GIDS dataloader accelerates the overall DGL GNN training pipeline by up to 582× when compared to the current, state-of-the-art DGL dataloader.

Publisher

Association for Computing Machinery (ACM)

Reference51 articles.

1. 2023. Nvidia ampere architecture in-depth. https://developer.nvidia.com/blog/nvidia-ampere-architecture-in-depth/

2. MG-GCN: A Scalable multi-GPU GCN Training Framework

3. Joan Bruna Wojciech Zaremba Arthur Szlam and Yann LeCun. 2014. Spectral Networks and Locally Connected Networks on Graphs. arXiv:1312.6203 [cs.LG]

4. Zhenkun Cai, Qihui Zhou, Xiao Yan, Da Zheng, Xiang Song, Chenguang Zheng, James Cheng, and George Karypis. 2023. DSP: Efficient GNN Training with Multiple GPUs. In Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (Montreal, QC, Canada) (PPoPP '23). 392--404.

5. Cluster-GCN

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3