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Graph neural networks (GNNs) are gaining increasing popularity as a promising approach to machine learning on graphs.
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J. E. Gonzalez, Y. Low, H. Gu, D. Bickson, and C. Guestrin, “Powergraph: Distributed graph-parallel computation on natural graphs,” USENIX Symp. on Operating Systems Design and Implementation (OSDI) , 2012
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M. Harris et al. , “Optimizing parallel reduction in cuda,” http://developer.download.nvidia.com/assets/cuda/files/reduction.pdf , 2012
2012
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2013
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J. Shun and G. E. Blelloch, “Ligra: A lightweight graph processing framework for shared memory,” ACM SIGPLAN Notices , 2013
2013
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J. Ragan-Kelley, C. Barnes, A. Adams, S. Paris, F. Durand, and S. Amarasinghe, “Halide: A language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines,” ACM SIGPLAN Notices , 2013
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2014
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H. Zhao, “High performance machine learning through codesign and rooflining,” Ph.D. dissertation, UC Berkeley, 2014
2014
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J. Ansel, S. Kamil, K. Veeramachaneni, J. Ragan-Kelley, J. Bosboom, U.-M. O’Reilly, and S. Amarasinghe, “Opentuner: An extensible framework for program autotuning,” Int’l Conf. on Parallel Architectures and Compilation Techniques (PACT) , 2014
2014
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D. Zheng, D. Mhembere, R. Burns, J. Vogelstein, C. E. Priebe, and A. S. Szalay, “Flashgraph: Processing billion-node graphs on an array of commodity ssds,” USENIX Conf. on File and Storage Technologies (FAST) , 2015
2015
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X. Zhu, W. Han, and W. Chen, “Gridgraph: Large-scale graph processing on a single machine using 2-level hierarchical partitioning,” USENIX Annual Technical Conf. (ATC) , 2015
2015
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F. Khorasani, R. Gupta, and L. N. Bhuyan, “Scalable simd-efficient graph processing on gpus,” Int’l Conf. on Parallel Architectures and Compilation Techniques (PACT) , 2015
2015
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F. McSherry, M. Isard, and D. G. Murray, “Scalability! but at what cost?” Workshop on Hot Topics in Operating Systems (HotOS) , 2015
2015
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N. Sundaram, N. Satish, M. M. A. Patwary, S. R. Dulloor, M. J. Anderson, S. G. Vadlamudi, D. Das, and P. Dubey, “Graphmat: High performance graph analytics made productive,” Int’l Conf. on Very Large Data Bases (VLDB) , 2015
2015
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M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard et al. , “Tensorflow: A system for large-scale machine learning,” USENIX Symp. on Operating Systems Design and Implementation (OSDI) , 2016
2016
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Y. Wang, A. Davidson, Y. Pan, Y. Wu, A. Riffel, and J. D. Owens, “Gunrock: A high-performance graph processing library on the gpu,” ACM SIGPLAN Notices , 2016
2016
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2016
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J. Kepner, P. Aaltonen, D. Bader, A. Buluç, F. Franchetti, J. Gilbert, D. Hutchison, M. Kumar, A. Lumsdaine, H. Meyerhenke et al. , “Mathematical foundations of the graphblas,” IEEE High Performance Extreme Computing Conf. (HPEC) , 2016
2016
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R. Palm, U. Paquet, and O. Winther, “Recurrent relational networks,” Conf. on Neural Information Processing Systems (NIPS) , 2018
2018
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T. Chen, T. Moreau, Z. Jiang, L. Zheng, E. Yan, H. Shen, M. Cowan, L. Wang, Y. Hu, L. Ceze et al. , “TVM: An automated end-to-end optimizing compiler for deep learning,” USENIX Symp. on Operating Systems Design and Implementation (OSDI) , 2018
2018
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2018
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2018
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D. Zheng, D. Mhembere, V. Lyzinski, J. T. Vogelstein, C. E. Priebe, and R. Burns, “Semi-external memory sparse matrix multiplication for billion-node graphs,” IEEE Trans. on Parallel and Distributed Systems (TPDS) , 2016
2016
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J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” Int’l Conf. on Machine Learning (ICML) , 2017
2017
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Y. Zhang, V. Kiriansky, C. Mendis, S. Amarasinghe, and M. Zaharia, “Making caches work for graph analytics,” IEEE Int’l Conf. on Big Data , 2017
2017
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A. Santoro, D. Raposo, D. G. Barrett, M. Malinowski, R. Pascanu, P. Battaglia, and T. Lillicrap, “A simple neural network module for relational reasoning,” Conf. on Neural Information Processing Systems (NIPS) , 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Conf. on Neural Information Processing Systems (NIPS) , 2017
2017
Cited alongside, same era.
W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” Conf. on Neural Information Processing Systems (NIPS) , 2017
2017
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F. Kjolstad, S. Kamil, S. Chou, D. Lugato, and S. Amarasinghe, “The tensor algebra compiler,” Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA) , 2017
2017
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C. Yang, A. Buluç, and J. D. Owens, “Design principles for sparse matrix multiplication on the gpu,” European Conf. on Parallel Processing (Euro-Par) , 2018
2018
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T. Chen, L. Zheng, E. Yan, Z. Jiang, T. Moreau, L. Ceze, C. Guestrin, and A. Krishnamurthy, “Learning to optimize tensor programs,” Conf. on Neural Information Processing Systems (NIPS) , 2018
2018
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2019
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L. Ma, Z. Yang, Y. Miao, J. Xue, M. Wu, L. Zhou, and Y. Dai, “Neugraph: Parallel deep neural network computation on large graphs,” USENIX Annual Technical Conf. (ATC) , 2019
2019
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2019
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A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al. , “Pytorch: An imperative style, high-performance deep learning library,” Conf. on Neural Information Processing Systems (NeurIPS) , 2019
2019
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2019
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Y. Liu, Y. Wang, R. Yu, M. Li, V. Sharma, and Y. Wang, “Optimizing cnn model inference on cpus,” USENIX Annual Technical Conf. (ATC) , 2019
2019
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L. Wang, Z. Chen, Y. Liu, Y. Wang, L. Zheng, M. Li, and Y. Wang, “A unified optimization approach for cnn model inference on integrated gpus,” Int’l Conf. on Parallel Processing (ICPP) , 2019
2019
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“Minigun: Light-weight gpu kernel interface for graph operations,” https://github.com/dglai/minigun , 2019
2019
Later among the works it cites.