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A graph neural network (GNN) enables deep learning on structured graph data.
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Powergraph: Distributed graph-parallel computation on natural graphs
J. E. Gonzalez, Y. Low, H. Gu, D. Bickson, and C. Guestrin · 2012
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GraphChi: Large-Scale Graph Computation on Just a PC
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TurboGraph: A fast parallel graph engine handling billion-scale graphs in a single PC
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More effective distributed ml via a stale synchronous parallel parameter server
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Naiad: A timely dataflow system
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A lightweight infrastructure for graph analytics
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X-Stream: Edge-centric graph processing using streaming partitions
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Ligra: A lightweight graph processing framework for shared memory
J. Shun and G. E. Blelloch · 2013
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Pregelix: Big(ger) graph analytics on a dataflow engine
Y. Bu, V. Borkar, J. Jia, M. J. Carey, and T. Condie · 2014
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Computation and communication efficient graph processing with distributed immutable view
R. Chen, X. Ding, P. Wang, H. Chen, B. Zang, and H. Guan · 2014
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cudnn: Efficient primitives for deep learning, 2014
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Project adam: Building an efficient and scalable deep learning training system
T. Chilimbi, Y. Suzue, J. Apacible, and K. Kalyanaraman · 2014
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Exploiting bounded staleness to speed up big data analytics
H. Cui, J. Cipar, Q. Ho, J. K. Kim, S. Lee, A. Kumar, J. Wei, W. Dai, G. R. Ganger, P. B. Gibbons, G. A. Gibson, and E. P. Xing · 2014
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Adam: A method for stochastic optimization, 2014
D. P. Kingma and J. Ba · 2014
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One weird trick for parallelizing convolutional neural networks
A. Krizhevsky · 2014
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Scaling distributed machine learning with the parameter server
M. Li, D. G. Andersen, J. W. Park, A. J. Smola, A. Ahmed, V. Josifovski, J. Long, E. J. Shekita, and B.-Y. Su · 2014
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MMap: Fast billion-scale graph computation on a pc via memory mapping
Z. Lin, M. Kahng, K. M. Sabrin, D. H. P. Chau, H. Lee, , and U. Kang · 2014
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1-bit stochastic gradient descent and application to data-parallel distributed training of speech dnns
F. Seide, H. Fu, J. Droppo, G. Li, and D. Yu · 2014
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On parallelizability of stochastic gradient descent for speech dnns
F. Seide, H. Fu, J. Droppo, G. Li, and D. Yu · 2014
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ASPIRE: Exploiting asynchronous parallelism in iterative algorithms using a relaxed consistency based dsm
K. Vora, S. C. Koduru, and R. Gupta · 2014
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PowerLyra: Differentiated graph computation and partitioning on skewed graphs
R. Chen, J. Shi, Y. Chen, and H. Chen · 2015
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Convolutional networks on graphs for learning molecular fingerprints
D. Duvenaud, D. Maclaurin, J. Aguilera-Iparraguirre, R. Gómez-Bombarelli, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Image-based recommendations on styles and substitutes
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Chaos: Scale-out graph processing from secondary storage
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GraphQ: Graph query processing with abstraction refinement—programmable and budget-aware analytical queries over very large graphs on a single PC
K. Wang, G. Xu, Z. Su, and Y. D. Liu · 2015
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GraM: Scaling graph computation to the trillions
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FlashGraph: processing billion-node graphs on an array of commodity ssds
D. Zheng, D. Mhembere, R. Burns, J. Vogelstein, C. E. Priebe, and A. S. Szalay · 2015
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GridGraph: Large scale graph processing on a single machine using 2-level hierarchical partitioning
X. Zhu, W. Han, and W. Chen · 2015
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Revisiting distributed synchronous sgd
J. Chen, R. Monga, S. Bengio, and R. Jozefowicz · 2016
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GeePS: Scalable deep learning on distributed GPUs with a gpu-specialized parameter server
H. Cui, H. Zhang, G. R. Ganger, P. B. Gibbons, and E. P. Xing · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
M. Defferrard, X. Bresson, and P. Vandergheynst · 2016
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Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
Rstream: Marrying relational algebra with streaming for efficient graph mining on a single machine
K. Wang, Z. Zuo, J. Thorpe, T. Q. Nguyen, and G. H. Xu · 2018
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Graph convolutional neural networks for web-scale recommender systems
R. Ying, R. He, K. Chen, P. Eksombatchai, W. L. Hamilton, and J. Leskovec · 2018
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GaAN: Gated attention networks for learning on large and spatiotemporal graphs
J. Zhang, X. Shi, J. Xie, H. Ma, I. King, and D. Yeung · 2018
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Graph neural networks: A review of methods and applications
J. Zhou, G. Cui, Z. Zhang, C. Yang, Z. Liu, and M. Sun · 2018
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Announcing improved vpc networking for aws lambda functions
A. AWS · 2019
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R. He and J. McAuley · 2016
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Gated graph sequence neural networks
Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel · 2016
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Gated graph sequence neural networks
Y. Li, D. Tarlow, M. Brockschmidt, and R. S. Zemel · 2016
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Fast and concurrent RDF queries with rdma-based distributed graph exploration
J. Shi, Y. Yao, R. Chen, H. Chen, and F. Li · 2016
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Synergistic analysis of evolving graphs
K. Vora, R. Gupta, and G. Xu · 2016
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Load the edges you need: A generic I/O optimization for disk-based graph processing
K. Vora, G. Xu, and R. Gupta · 2016
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Exploring the hidden dimension in graph processing
M. Zhang, Y. Wu, K. Chen, X. Qian, X. Li, and W. Zheng · 2016
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Analysis of dawnbench, a time-to-accuracy machine learning performance benchmark
C. Coleman, D. Kang, D. Narayanan, L. Nardi, T. Zhao, J. Zhang, P. Bailis, K. Olukotun, C. Ré, and M. Zaharia · 2019
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Serverless computing: One step forward, two steps back
J. M. Hellerstein, J. M. Faleiro, J. Gonzalez, J. Schleier-Smith, V. Sreekanti, A. Tumanov, and C. Wu · 2019
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Key trends from NeurIPS 2019
C. Huyen · 2019
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Beyond data and model parallelism for deep neural networks
Z. Jia, M. Zaharia, and A. Aiken · 2019
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Graph convolutional networks with motif-based attention
J. B. Lee, R. A. Rossi, X. Kong, S. Kim, E. Koh, and A. Rao · 2019
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Hyperbolic graph neural networks
Q. Liu, M. Nickel, and D. Kiela · 2019
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NeuGraph: Parallel deep neural network computation on large graphs
L. Ma, Z. Yang, Y. Miao, J. Xue, M. Wu, L. Zhou, and Y. Dai · 2019
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GraphBolt: Dependency-Driven Synchronous Processing of Streaming Graphs
M. Mariappan and K. Vora · 2019
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PipeDream: Generalized pipeline parallelism for DNN training
D. Narayanan, A. Harlap, A. Phanishayee, V. Seshadri, N. R. Devanur, G. R. Ganger, P. B. Gibbons, and M. Zaharia · 2019
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LUMOS: Dependency-driven disk-based graph processing
K. Vora · 2019
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PharML.Bind: Pharmacologic machine learning for protein-ligand interactions, 2019
A. D. Vose, J. Balma, D. Farnsworth, K. Anderson, and Y. K. Peterson · 2019
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Deep graph library: Towards efficient and scalable deep learning on graphs
M. Wang, L. Yu, D. Zheng, Q. Gan, Y. Gai, Z. Ye, M. Li, J. Zhou, Q. Huang, C. Ma, Z. Huang, Q. Guo, H. Zhang, H. Lin, J. Zhao, J. Li, A. J. Smola, and Z. Zhang · 2019
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Session-based recommendation with graph neural networks
S. Wu, Y. Tang, Y. Zhu, L. Wang, X. Xie, and T. Tan · 2019
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A comprehensive survey on graph neural networks
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu · 2019
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OpenBLAS
Z. Xianyi and M. Kroeker · 2019
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Aligraph: A comprehensive graph neural network platform
H. Yang · 2019
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Graph transformer networks
S. Yun, M. Jeong, R. Kim, J. Kang, and H. J. Kim · 2019
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AWS Lambda Pricing
Amazon · 2020
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Checkmate: Breaking the memory wall with optimal tensor rematerialization
P. Jain, A. Jain, A. Nrusimha, A. Gholami, P. Abbeel, J. Gonzalez, K. Keutzer, and I. Stoica · 2020
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Improving the accuracy, scalability, and performance of graph neural networks with Roc
Z. Jia, S. Lin, M. Gao, M. Zaharia, and A. Aiken · 2020
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Redundancy-free computation for graph neural networks
Z. Jia, S. Lin, R. Ying, J. You, J. Leskovec, and A. Aiken · 2020
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Trends and fads in machine learning – topics on the rise and in decline in ICLR submissions
M. Kustosz and B. Osinski · 2020
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Stanford network analysis project
J. Leskovec · 2020
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cusparse
NVIDIA · 2020
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The reddit datasets
Reddit · 2020
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ZeroMQ networking library for C++
ZeroMQ · 2020
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AGL: A scalable system for industrial-purpose graph machine learning
D. Zhang, X. Huang, Z. Liu, J. Zhou, Z. Hu, X. Song, Z. Ge, L. Wang, Z. Zhang, and Y. Qi · 2020
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Dynamic tensor rematerialization
M. Kirisame, S. Lyubomirsky, A. Haan, J. Brennan, M. He, J. Roesch, T. Chen, and Z. Tatlock · 2021
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DZiG: Sparsity-Aware Incremental Processing of Streaming Graphs
M. Mariappan, J. Che, and K. Vora · 2021
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