Characterizing the Scalability of Graph Convolutional Networks on Intel<sup>®</sup> PIUMA-Reference-Cited by-同舟云学术

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Characterizing the Scalability of Graph Convolutional Networks on Intel^® PIUMA

Published:2023-04 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)
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Author:

Adiletta Matthew Joseph¹,Tithi Jesmin Jahan²,Farsarakis Emmanouil-Ioannis²,Gerogiannis Gerasimos²,Adolf Robert¹,Benke Robert²,Kashyap Sidharth²,Hsia Samuel¹,Lakhotia Kartik²,Petrini Fabrizio¹,Wei Gu-Yeon¹,Brooks David¹

Affiliation:

1. Harvard University

2. Intel Corporation

Funder

Defense Advanced Research Projects Agency

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10158063/10158070/10158124.pdf?arnumber=10158124

Reference32 articles.

1. Architectural Implication of Graph Neural Networks

2. Distributed-memory parallel algorithms for sparse times tall-skinny-dense matrix multiplication

3. Characterizing and Understanding GCNs on GPU

4. Rubik: A Hierarchical Architecture for Efficient Graph Neural Network Training

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Two-Face: Combining Collective and One-Sided Communication for Efficient Distributed SpMM;Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2;2024-04-27

2. HotTiles: Accelerating SpMM with Heterogeneous Accelerator Architectures;2024 IEEE International Symposium on High-Performance Computer Architecture (HPCA);2024-03-02

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