research-article

Open access

Understanding and bridging the gaps in current GNN performance optimizations

Authors:

Xipeng ShenAuthors Info & Claims

PPoPP '21: Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

Pages 119 - 132

https://doi.org/10.1145/3437801.3441585

Published: 17 February 2021 Publication History

Abstract

Graph Neural Network (GNN) has recently drawn a rapid increase of interest in many domains for its effectiveness in learning over graphs. Maximizing its performance is essential for many tasks, but remains preliminarily understood. In this work, we provide an in-depth examination of the state-of-the-art GNN frameworks, revealing five major gaps in the current frameworks in optimizing GNN performance, especially in handling the special complexities of GNN over traditional graph or DNN operations. Based on the insights, we put together a set of optimizations to fill the gaps. These optimizations leverage the state-of-the-art GPU optimization techniques and tailor them to the special properties of GNN. Experimental results show that these optimizations achieve 1.37×--15.5× performance improvement over the state-of-the-art frameworks on various GNN models.

References

[1]

Tal Ben-Nun, Michael Sutton, Sreepathi Pai, and Keshav Pingali. 2017. Groute: An Asynchronous Multi-GPU Programming Model for Irregular Computations. In Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (Austin, Texas, USA) (PPoPP '17). Association for Computing Machinery, New York, NY, USA, 235--248.

Digital Library

[2]

Yukuo Cen, Zhenyu Hou, Yan Wang, Qibin Chen, and Jie Tang. 2020. CogDL: An Extensive Research Toolkit for Deep Learning on Graphs. https://github.com/thudm/cogdl

[3]

Wei-Lin Chiang, Xuanqing Liu, Si Si, Yang Li, Samy Bengio, and Cho-Jui Hsieh. 2019. Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery; Data Mining (Anchorage, AK, USA) (KDD '19). Association for Computing Machinery, New York, NY, USA, 257--266.

Digital Library

[4]

Roshan Dathathri, Gurbinder Gill, Loc Hoang, Hoang-Vu Dang, Alex Brooks, Nikoli Dryden, Marc Snir, and Keshav Pingali. 2018. Gluon: A Communication-Optimizing Substrate for Distributed Heterogeneous Graph Analytics. In Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation (Philadelphia, PA, USA) (PLDI 2018). Association for Computing Machinery, New York, NY, USA, 752--768.

Digital Library

[5]

Matthias Fey and Jan Eric Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. CoRR abs/1903.02428 (2019). arXiv:1903.02428 http://arxiv.org/abs/1903.02428

[6]

Trevor Gale, Matei Zaharia, Cliff Young, and Erich Elsen. 2020. Sparse GPU Kernels for Deep Learning., Article 17 (2020), 14 pages.

[7]

Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, and Yue Hu. 2020. Graph Neural Architecture Search. (7 2020), 1403--1409. Main track.

[8]

Thomas Gaudelet, Ben Day, Arian R. Jamasb, Jyothish Soman, Cristian Regep, Gertrude Liu, Jeremy B. R. Hayter, Richard Vickers, Charles Roberts, Jian Tang, David Roblin, Tom L. Blundell, Michael M. Bronstein, and Jake P. Taylor-King. 2020. Utilising Graph Machine Learning within Drug Discovery and Development. arXiv:2012.05716 [q-bio.QM]

[9]

Aditya Grover and Jure Leskovec. 2016. Node2vec: Scalable Feature Learning for Networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, California, USA) (KDD '16). Association for Computing Machinery, New York, NY, USA, 855--864.

Digital Library

[10]

William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS'17). Curran Associates Inc., Red Hook, NY, USA, 1025--1035.

Digital Library

[11]

Changwan Hong, Aravind Sukumaran-Rajam, Israt Nisa, Kunal Singh, and P. Sadayappan. 2019. Adaptive Sparse Tiling for Sparse Matrix Multiplication. In Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming (Washington, District of Columbia) (PPoPP '19). Association for Computing Machinery, New York, NY, USA, 300--314.

Digital Library

[12]

Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020. Open Graph Benchmark: Datasets for Machine Learning on Graphs. (2020). https://proceedings.neurips.cc/paper/2020/hash/fb60d411a5c5b72b2e7d3527cfic84fd0-Abstract.html

[13]

Zhihao Jia, Yongkee Kwon, Galen M. Shipman, Patrick S. McCormick, Mattan Erez, and Alex Aiken. 2017. A Distributed Multi-GPU System for Fast Graph Processing. Proc. VLDB Endow. 11, 3 (2017), 297--310.

Digital Library

[14]

Zhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, and Alex Aiken. 2020. Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc. In Proceedings of Machine Learning and Systems 2020, MLSys 2020, March 2-4, 2020, Inderjit S. Dhillon, Dimitris S. Papailiopoulos, and Vivienne Sze (Eds.). mlsys.org, Austin, TX, USA, 187--198. https://proceedings.mlsys.org/book/300.pdf

[15]

Zhihao Jia, Sina Lin, Rex Ying, Jiaxuan You, Jure Leskovec, and Alex Aiken. 2020. Redundancy-Free Computation for Graph Neural Networks. (2020), 997--1005.

Digital Library

[16]

Zhihao Jia, Oded Padon, James Thomas, Todd Warszawski, Matei Zaharia, and Alex Aiken. 2019. TASO: Optimizing Deep Learning Computation with Automatic Generation of Graph Substitutions. In Proceedings of the 27th ACM Symposium on Operating Systems Principles (Huntsville, Ontario, Canada) (SOSP '19). Association for Computing Machinery, New York, NY, USA, 47--62.

Digital Library

[17]

Zhihao Jia, James J. Thomas, Todd Warszawski, Mingyu Gao, Matei Zaharia, and Alex Aiken. 2019. Optimizing DNN Computation with Relaxed Graph Substitutions. In Proceedings of Machine Learning and Systems 2019, MLSys 2019, Stanford, CA, USA, March 31 - April 2, 2019, Ameet Talwalkar, Virginia Smith, and Matei Zaharia (Eds.). mlsys.org, Stanford, CA, USA. https://proceedings.mlsys.org/book/276.pdf

[18]

Zhihao Jia, Matei Zaharia, and Alex Aiken. 2019. Beyond Data and Model Parallelism for Deep Neural Networks. 1 (2019), 1--13. https://proceedings.mlsys.org/paper/2019/file/c74d97b01eae257e44aa9d5bade97baf-Paper.pdf

[19]

Peng Jiang, Changwan Hong, and Gagan Agrawal. 2020. A Novel Data Transformation and Execution Strategy for Accelerating Sparse Matrix Multiplication on GPUs. In Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (San Diego, California) (PPoPP '20). Association for Computing Machinery, New York, NY, USA, 376--388.

Digital Library

[20]

Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In Proceedings of the 5th International Conference on Learning Representations (Palais des Congrès Neptune, Toulon, France) (ICLR '17). Palais des Congrès Neptune, Toulon, France. https://openreview.net/forum?id=SJU4ayYgl

[21]

Fredrik Kjolstad, Stephen Chou, David Lugato, Shoaib Kamil, and Saman Amarasinghe. 2017. Taco: A Tool to Generate Tensor Algebra Kernels. In Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering (Urbana-Champaign, IL, USA) (ASE 2017). IEEE Press, Urbana-Champaign, IL, USA, 943--948.

Digital Library

[22]

Chris Leary and Todd Wang. 2017. XLA: TensorFlow, compiled. TensorFlow Dev Summit (2017).

[23]

Adam Lerer, Ledell Wu, Jiajun Shen, Timothée Lacroix, Luca Wehrstedt, Abhijit Bose, and Alexander Peysakhovich. 2019. PyTorch-BigGraph: A Large-scale Graph Embedding System. CoRR abs/1903.12287 (2019). arXiv:1903.12287 http://arxiv.org/abs/1903.12287

[24]

Jure Leskovec, Anand Rajaraman, and Jeffrey D. Ullman. 2014. Mining of Massive Datasets, 2nd Ed. Cambridge University Press. http://www.mmds.org/

Digital Library

[25]

G. Li, M. Muller, A. Thabet, and B. Ghanem. 2019. DeepGCNs: Can GCNs Go As Deep As CNNs?. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE Computer Society, Los Alamitos, CA, USA, 9266--9275.

[26]

Guohao Li, Chenxin Xiong, Ali K. Thabet, and Bernard Ghanem. 2020. DeeperGCN: All You Need to Train Deeper GCNs. CoRR abs/2006.07739 (2020). arXiv:2006.07739 https://arxiv.org/abs/2006.07739

[27]

Husong Liu, Shengliang Lu, Xinyu Chen, and Bingsheng He. 2020. G3: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs. Proc. VLDB Endow. 13, 12 (2020), 2813--2816. http://www.vldb.org/pvldb/vol13/p2813-liu.pdf

Digital Library

[28]

Meng Liu, Hongyang Gao, and Shuiwang Ji. 2020. Towards Deeper Graph Neural Networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery; Data Mining (Virtual Event, CA, USA) (KDD '20). Association for Computing Machinery, New York, NY, USA, 338--348.

Digital Library

[29]

Lingxiao Ma, Zhi Yang, Youshan Miao, Jilong Xue, Ming Wu, Lidong Zhou, and Yafei Dai. 2019. NeuGraph: Parallel Deep Neural Network Computation on Large Graphs. In 2019 USENIX Annual Technical Conference (USENIX ATC 19). USENIX Association, Renton, WA, 443--458. https://www.usenix.org/conference/atc19/presentation/ma

[30]

D. Merrill and M. Garland. 2016. Merge-Based Parallel Sparse Matrix-Vector Multiplication. In SC '16: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 678--689.

[31]

Dmitry Molchanov, Arsenii Ashukha, and Dmitry Vetrov. 2017. Variational Dropout Sparsifies Deep Neural Networks. In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (Sydney, NSW, Australia) (ICML'17). JMLR.org, Sydney, NSW, Australia, 2498--2507.

[32]

Sharan Narang, Eric Undersander, and Gregory F. Diamos. 2017. Block-Sparse Recurrent Neural Networks. CoRR abs/1711.02782 (2017). arXiv:1711.02782 http://arxiv.org/abs/1711.02782

[33]

Nvidia. 2020. The API reference guide for cuSPARSE. https://docs.nvidia.com/cuda/cusparse/index.html

[34]

Yosuke Oyama, Tal Ben-Nun, Torsten Hoefler, and Satoshi Matsuoka. 2018. Accelerating Deep Learning Frameworks with Micro-Batches. In IEEE International Conference on Cluster Computing, CLUSTER 2018, Belfast, UK, September 10-13, 2018. IEEE Computer Society, Belfast, UK, 402--412.

[35]

Mathias Parger, Martin Winter, Daniel Mlakar, and Markus Steinberger. 2020. SpECK: Accelerating GPU Sparse Matrix-Matrix Multiplication through Lightweight Analysis. In Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (San Diego, California) (PPoPP '20). Association for Computing Machinery, New York, NY, USA, 362--375.

Digital Library

[36]

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems, H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Eds.), Vol. 32. Curran Associates, Inc., 8026--8037. https://proceedings.neurips.cc/paper/2019/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf

[37]

Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. DeepWalk: Online Learning of Social Representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (New York, New York, USA) (KDD '14). Association for Computing Machinery, New York, NY, USA, 701--710.

Digital Library

[38]

Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, and Tina Eliassi-Rad. 2008. Collective Classification in Network Data. AI Mag. 29, 3 (2008), 93--106.

Digital Library

[39]

Zequn Sun, Chengming Wang, Wei Hu, Muhao Chen, Jian Dai, Wei Zhang, and Yuzhong Qu. 2020. Knowledge Graph Alignment Network with Gated Multi-Hop Neighborhood Aggregation. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, February 7-12, 2020. AAAI Press, New York, NY, USA, 222--229. https://aaai.org/ojs/index.php/AAAI/article/view/5354

[40]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, undefinedukasz Kaiser, and Illia Polosukhin. 2017. Attention is All You Need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (Long Beach, California, USA) (NIPS'17). Curran Associates Inc., Red Hook, NY, USA, 6000--6010.

Digital Library

[41]

Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2018. Graph Attention Networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net, Vancouver, BC, Canada. https://openreview.net/forum?id=rJXMpikCZ

[42]

Minjie Wang, Lingfan Yu, Da Zheng, Quan Gan, Yu Gai, Zihao Ye, Mufei Li, Jinjing Zhou, Qi Huang, Chao Ma, Ziyue Huang, Qipeng Guo, Hao Zhang, Haibin Lin, Junbo Zhao, Jinyang Li, Alexander J. Smola, and Zheng Zhang. 2019. Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs. CoRR abs/1909.01315 (2019). arXiv:1909.01315 http://arxiv.org/abs/1909.01315

[43]

Yangzihao Wang, Andrew Davidson, Yuechao Pan, Yuduo Wu, Andy Riffel, and John D. Owens. 2016. Gunrock: A High-Performance Graph Processing Library on the GPU. SIGPLAN Not. 51, 8, Article 11, 12 pages.

Digital Library

[44]

Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu. 2020. A Comprehensive Survey on Graph Neural Networks. IEEE Transactions on Neural Networks and Learning Systems (2020), 1--21.

[45]

Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net, New Orleans, LA, USA. https://openreview.net/forum?id=ryGs6iA5Km

[46]

Carl Yang, Aydin Buluç, and John D. Owens. 2018. Design Principles for Sparse Matrix Multiplication on the GPU. In Euro-Par 2018: Parallel Processing - 24th International Conference on Parallel and Distributed Computing, August 27-31, 2018, Proceedings (Lecture Notes in Computer Science, Vol. 11014), Marco Aldinucci, Luca Padovani, and Massimo Torquati (Eds.). Springer, Turin, Italy, 672--687.

[47]

Carl Yang, Aydin Buluç, and John D. Owens. 2019. GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU. CoRR abs/1908.01407 (2019). arXiv:1908.01407 http://arxiv.org/abs/1908.01407

[48]

Zhuliang Yao, Shijie Cao, Wencong Xiao, Chen Zhang, and Lanshun Nie. 2019. Balanced Sparsity for Efficient DNN Inference on GPU. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019. AAAI Press, Honolulu, Hawaii, USA, 5676--5683.

Digital Library

[49]

Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor K. Prasanna. 2020. GraphSAINT: Graph Sampling Based Inductive Learning Method. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net, Addis Ababa, Ethiopia. https://openreview.net/forum?id=BJe8pkHFwS

[50]

Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, and Dit-Yan Yeung. 2018. GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs. In Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, UAI 2018, Monterey, California, USA, August 6-10, 2018, Amir Globerson and Ricardo Silva (Eds.). AUAI Press, Monterey, California, USA, 339--349. http://auai.org/uai2018/proceedings/papers/139.pdf

[51]

Zhen Zheng, Pengzhan Zhao, Guoping Long, Feiwen Zhu, Kai Zhu, Wenyi Zhao, Lansong Diao, Jun Yang, and Wei Lin. 2020. FusionStitching: Boosting Memory Intensive Computations for Deep Learning Workloads. CoRR abs/2009.10924 (2020). arXiv:2009.10924 https://arxiv.org/abs/2009.10924

[52]

Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, and Jingren Zhou. 2019. AliGraph: A Comprehensive Graph Neural Network Platform. Proc. VLDB Endow. 12, 12 (2019), 2094--2105.

Digital Library

Cited By

Ai XWang QCao CZhang YChen CYuan HGu YYu G(2024)NeutronOrch: Rethinking Sample-Based GNN Training under CPU-GPU Heterogeneous EnvironmentsProceedings of the VLDB Endowment10.14778/3659437.365945317:8(1995-2008)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.14778/3659437.3659453
Yuan HLiu YZhang YAi XWang QChen CGu YYu G(2024)Comprehensive Evaluation of GNN Training Systems: A Data Management PerspectiveProceedings of the VLDB Endowment10.14778/3648160.364816717:6(1241-1254)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.14778/3648160.3648167
Tang DWang JChen RWang LYu WZhou JLi K(2024)XGNN: Boosting Multi-GPU GNN Training via Global GNN Memory StoreProceedings of the VLDB Endowment10.14778/3641204.364121917:5(1105-1118)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.14778/3641204.3641219
Show More Cited By

Index Terms

Understanding and bridging the gaps in current GNN performance optimizations
1. Computer systems organization
  1. Architectures
    1. Parallel architectures
      1. Single instruction, multiple data
2. Computing methodologies
  1. Parallel computing methodologies
    1. Parallel algorithms
      1. Massively parallel algorithms

Recommendations

Tackling Oversmoothing in GNN via Graph Sparsification
Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track
Abstract
Graph Neural Network (GNN) achieves great success for node-level and graph-level tasks via encoding meaningful topological structures of networks in various domains, ranging from social to biological networks. However, repeated aggregation ...
WiseGraph: Optimizing GNN with Joint Workload Partition of Graph and Operations
EuroSys '24: Proceedings of the Nineteenth European Conference on Computer Systems

Graph Neural Network (GNN) has emerged as an important workload for learning on graphs. With the size of graph data and the complexity of GNN model architectures increasing, developing an efficient GNN system grows more important. As GNN has heavy neural ...
A Unified CPU-GPU Protocol for GNN Training
CF '24: Proceedings of the 21st ACM International Conference on Computing Frontiers

Training a Graph Neural Network (GNN) model on large-scale graphs involves a high volume of data communication and computations. While state-of-the-art CPUs and GPUs feature high computing power, the Standard GNN training protocol adopted in existing GNN ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

PPoPP '21: Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

February 2021

507 pages

ISBN:9781450382946

DOI:10.1145/3437801

General Chair:
Jaejin Lee
Seoul National University, South Korea
,
Program Chair:
Erez Petrank
Technion, Israel

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 February 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Badges

Author Tags

Qualifiers

Research-article

Conference

PPoPP '21

Sponsor:

PPoPP '21: 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

February 27, 2021

Virtual Event, Republic of Korea

Acceptance Rates

PPoPP '21 Paper Acceptance Rate 31 of 150 submissions, 21%;

Overall Acceptance Rate 230 of 1,014 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

55
Total Citations
View Citations
4,791
Total Downloads

Downloads (Last 12 months)1,078
Downloads (Last 6 weeks)138

Reflects downloads up to 25 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ai XWang QCao CZhang YChen CYuan HGu YYu G(2024)NeutronOrch: Rethinking Sample-Based GNN Training under CPU-GPU Heterogeneous EnvironmentsProceedings of the VLDB Endowment10.14778/3659437.365945317:8(1995-2008)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.14778/3659437.3659453
Yuan HLiu YZhang YAi XWang QChen CGu YYu G(2024)Comprehensive Evaluation of GNN Training Systems: A Data Management PerspectiveProceedings of the VLDB Endowment10.14778/3648160.364816717:6(1241-1254)Online publication date: 1-Feb-2024
https://dl.acm.org/doi/10.14778/3648160.3648167
Tang DWang JChen RWang LYu WZhou JLi K(2024)XGNN: Boosting Multi-GPU GNN Training via Global GNN Memory StoreProceedings of the VLDB Endowment10.14778/3641204.364121917:5(1105-1118)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.14778/3641204.3641219
Giannoula CYang PFernandez IYang JDurvasula SLi YSadrosadati MLuna JMutlu OPekhimenko G(2024)PyGim : An Efficient Graph Neural Network Library for Real Processing-In-Memory ArchitecturesProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/37004348:3(1-36)Online publication date: 13-Dec-2024
https://doi.org/10.1145/3700434
Shi LYang JLv PYuan LKou FLuo JXu MCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Self-derived Knowledge Graph Contrastive Learning for RecommendationProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681693(7571-7580)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681693
Zhou KSubramanian KLin PFey MYin BLi J(2024)FASTEN: Fast GPU-accelerated Segmented Matrix Multiplication for Heterogenous Graph Neural NetworksProceedings of the 38th ACM International Conference on Supercomputing10.1145/3650200.3656593(511-524)Online publication date: 30-May-2024
https://dl.acm.org/doi/10.1145/3650200.3656593
Shao YLi HGu XYin HLi YMiao XZhang WCui BChen L(2024)Distributed Graph Neural Network Training: A SurveyACM Computing Surveys10.1145/364835856:8(1-39)Online publication date: 10-Apr-2024
https://dl.acm.org/doi/10.1145/3648358
Fu QJi YRolinger THuang H(2024)TLPGNN: A Lightweight Two-level Parallelism Paradigm for Graph Neural Network Computation on Single and Multiple GPUsACM Transactions on Parallel Computing10.1145/364471211:2(1-28)Online publication date: 8-Jun-2024
https://dl.acm.org/doi/10.1145/3644712
Huang KZhai JZheng LWang HJin YZhang QZhang RZheng ZYi YShen X(2024)WiseGraph: Optimizing GNN with Joint Workload Partition of Graph and OperationsProceedings of the Nineteenth European Conference on Computer Systems10.1145/3627703.3650063(1-17)Online publication date: 22-Apr-2024
https://dl.acm.org/doi/10.1145/3627703.3650063
Gong YKumar PMencagli GDazzi PLowenthal DBadia R(2024)GNNOne: A Unified System Optimizations for GNN KernelsProceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing10.1145/3625549.3658655(15-27)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3625549.3658655
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Figures

Tables

Media

View Table of Conten