MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators | Paper Citation Record

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This paper cites TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Reference 1

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Observation d1e82c4d-b714-4cef-9c66-73a1f30aeca0 · outbound

This paper cites Hardware accel- eration of graph neural networks.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Hardware accel- eration of graph neural networks

Reference 2

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This paper cites Staleness-Alleviated Distributed GNN Training via Online Dynamic-Embedding Prediction.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Staleness-Alleviated Distributed GNN Training via Online Dynamic-Embedding Prediction

Reference 3

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Observation e5c96f4a-508a-488b-b7d7-4d6674f254ff · outbound

This paper cites Pathways: Asynchronous distributed dataflow for ml.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Pathways: Asynchronous distributed dataflow for ml

Reference 4

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This paper cites Distributed Graph Neural Network Training with Periodic Stale Representation Synchronization.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Distributed Graph Neural Network Training with Periodic Stale Representation Synchronization

Reference 5

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Observation 81aa9a1f-8148-42a1-84b6-a145c7c84759 · outbound

This paper cites Dygnn: Algorithm and architecture support of dynamic pruning for graph neural net- works.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Dygnn: Algorithm and architecture support of dynamic pruning for graph neural net- works

Reference 6

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Observation e951a0f3-c9e8-4880-a91d-24df1d98633d · outbound

This paper cites Graph representation learning: a survey.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Graph representation learning: a survey

Reference 7

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This paper cites MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

Reference 8

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Observation 265278f4-a1ab-461e-9c46-63c206dce10c · outbound

This paper cites Rubik: A hierarchical architecture for efficient graph neural network training.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Rubik: A hierarchical architecture for efficient graph neural network training

Reference 9

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Observation f1dfa1f2-f91a-4a3a-a18f-fe22a470139d · outbound

This paper cites The bandwidth problem for graphs and matrices—a survey.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators The bandwidth problem for graphs and matrices—a survey

Reference 10

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Observation 900c8cf9-a8eb-46bf-8836-e18c85657a60 · outbound

This paper cites Reducing the bandwidth of sparse symmetric matrices.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Reducing the bandwidth of sparse symmetric matrices

Reference 11

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Observation 3c5b3bc4-135b-4bf8-b227-e9ef3509220c · outbound

This paper cites Hardware acceleration of sparse and irregular tensor computations of ml models: A survey and insights.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Hardware acceleration of sparse and irregular tensor computations of ml models: A survey and insights

Reference 12

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Observation 83f84d04-ce23-495c-a5e2-f37b943c8769 · outbound

This paper cites Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

Reference 13

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Observation f0113c2e-fa3e-447b-9752-ce615c03ca3a · outbound

This paper cites GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings

Reference 14

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Observation 0651de6c-f8bc-472b-a9f7-ca3cd0786885 · outbound

This paper cites Tlpgnn: A lightweight two- level parallelism paradigm for graph neural network computation on gpu.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Tlpgnn: A lightweight two- level parallelism paradigm for graph neural network computation on gpu

Reference 15

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Observation bef0258f-a19c-4a8e-a99f-3d345a12397c · outbound

This paper cites P3: Distributed deep graph learning at scale.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators P3: Distributed deep graph learning at scale

Reference 16

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Observation ddb8f1d2-30fb-4930-9934-726a654b23c9 · outbound

This paper cites Understanding the Design-Space of Sparse/Dense Multiphase GNN dataflows on Spatial Accelerators.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Understanding the Design-Space of Sparse/Dense Multiphase GNN dataflows on Spatial Accelerators

Reference 17

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Observation d30f8ec6-fb6f-4104-a5b9-a4e777ee08fb · outbound

This paper cites Awb-gcn: A graph convolutional network accelerator with runtime workload rebalancing.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Awb-gcn: A graph convolutional network accelerator with runtime workload rebalancing

Reference 18

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Observation 1dd4b653-4062-4595-b968-f8daa27bc569 · outbound

This paper cites I-gcn: A graph convolutional network accelerator with runtime locality enhancement through islandization.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators I-gcn: A graph convolutional network accelerator with runtime locality enhancement through islandization

Reference 19

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Observation 1c2058d1-43f6-4f1b-8054-b49b731acfe8 · outbound

This paper cites Data-efficient graph grammar learning for molecu- lar generation.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Data-efficient graph grammar learning for molecu- lar generation

Reference 20

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Observation d8d3438f-15be-42e0-a6a2-0bfc03f92b6c · outbound

This paper cites Inductive represen- tation learning on large graphs.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Inductive represen- tation learning on large graphs

Reference 21

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Observation 0d08922e-937e-4b4f-8655-414507764227 · outbound

This paper cites PipeDream: Fast and Efficient Pipeline Parallel DNN Training.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators PipeDream: Fast and Efficient Pipeline Parallel DNN Training

Reference 22

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Observation 08c0e4d8-8ded-42a4-9b44-487f86315581 · outbound

This paper cites Open Graph Benchmark: Datasets for Machine Learning on Graphs.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Open Graph Benchmark: Datasets for Machine Learning on Graphs

Reference 23

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Observation 2934b5ee-8cfa-4ddd-b4e2-150e63895935 · outbound

This paper cites Recurrent graph convolutional network-based multi- task transient stability assessment framework in power system.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Recurrent graph convolutional network-based multi- task transient stability assessment framework in power system

Reference 24

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Observation a1a61f0a-9771-4f8d-a903-250a3949abdb · outbound

This paper cites Wisegraph: Optimizing gnn with joint workload partition of graph and operations.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Wisegraph: Optimizing gnn with joint workload partition of graph and operations

Reference 25

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This paper cites Gpipe: Efficient training of giant neural networks using pipeline parallelism.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Gpipe: Efficient training of giant neural networks using pipeline parallelism

Reference 26

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Observation f14914ea-757b-45b0-943f-5a814e9559a1 · outbound

This paper cites GraphPipe: Improving Performance and Scalability of DNN Training with Graph Pipeline Parallelism.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators GraphPipe: Improving Performance and Scalability of DNN Training with Graph Pipeline Parallelism

Reference 27

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This paper cites A survey on knowledge graphs: Representation, acquisition, and applications.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators A survey on knowledge graphs: Representation, acquisition, and applications

Reference 28

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This paper cites Improving the accuracy, scalability, and performance of graph neural networks with roc.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Improving the accuracy, scalability, and performance of graph neural networks with roc

Reference 29

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This paper cites A survey of frequent subgraph mining algorithms.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators A survey of frequent subgraph mining algorithms

Reference 30

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This paper cites A unified architecture for accelerating distributed{DNN} 12 training in heterogeneous{GPU/CPU} clusters.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators A unified architecture for accelerating distributed{DNN} 12 training in heterogeneous{GPU/CPU} clusters

Reference 31

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This paper cites In-datacenter performance analysis of a tensor pro- cessing unit.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators In-datacenter performance analysis of a tensor pro- cessing unit

Reference 32

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This paper cites A fast and high quality multilevel scheme for partitioning irregular graphs.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators A fast and high quality multilevel scheme for partitioning irregular graphs

Reference 33

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Observation 6c470d37-b9c5-47f3-a7b0-fe75d054b951 · outbound

This paper cites GRIP: A Graph Neural Network Accelerator Architecture.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators GRIP: A Graph Neural Network Accelerator Architecture

Reference 34

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This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Semi-Supervised Classification with Graph Convolutional Networks

Reference 35

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Observation a5d7c67d-96a7-4e0e-b2ef-24c445cd0cf4 · outbound

This paper cites What is twitter, a social network or a news media? InProceedings of the 19th international conference on World wide web , pages 591–600, 2010.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators What is twitter, a social network or a news media? InProceedings of the 19th international conference on World wide web , pages 591–600, 2010

Reference 36

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This paper cites Maeri: En- abling flexible dataflow mapping over dnn accelerators via reconfig- urable interconnects.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Maeri: En- abling flexible dataflow mapping over dnn accelerators via reconfig- urable interconnects

Reference 37

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This paper cites GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Reference 38

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This paper cites Gcnax: A flexible and energy-efficient accelerator for graph convolutional neural networks.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Gcnax: A flexible and energy-efficient accelerator for graph convolutional neural networks

Reference 39

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Observation 20c0a3b0-4df1-44ea-969e-fd7f04788898 · outbound

This paper cites PyTorch Distributed: Experiences on Accelerating Data Parallel Training.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators PyTorch Distributed: Experiences on Accelerating Data Parallel Training

Reference 40

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Observation 1f7421e2-3a0b-4046-a37d-35e0bb6c16f3 · outbound

This paper cites Terapipe: Token-level pipeline parallelism for training large-scale language models.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Terapipe: Token-level pipeline parallelism for training large-scale language models

Reference 41

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This paper cites Engn: A high-throughput and energy-efficient accelerator for large graph neural networks.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Engn: A high-throughput and energy-efficient accelerator for large graph neural networks

Reference 42

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This paper cites Nvidia tesla: A unified graphics and computing architecture.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Nvidia tesla: A unified graphics and computing architecture

Reference 43

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Observation f98bde13-0dfc-4c84-a9cf-127e05790520 · outbound

This paper cites Flexflow: A flexible dataflow accelerator architecture for convolutional neural networks.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Flexflow: A flexible dataflow accelerator architecture for convolutional neural networks

Reference 44

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Observation 490a054e-7ca0-47b8-a6ec-a7c963318011 · outbound

This paper cites NeuGraph: Parallel deep neural network compu- tation on large graphs.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators NeuGraph: Parallel deep neural network compu- tation on large graphs

Reference 45

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Observation 3978c9b6-e306-4a77-a3a5-a133550d9d88 · outbound

This paper cites All-to-all personalized communication on multi- stage interconnection networks.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators All-to-all personalized communication on multi- stage interconnection networks

Reference 46

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Observation 6d4041c5-da6a-4b3c-8e98-05c1ebcd25d3 · outbound

This paper cites Distgnn: Scalable distributed training for large-scale graph neural networks.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Distgnn: Scalable distributed training for large-scale graph neural networks

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:02.447000Z

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Observation 319f8905-194a-4c96-ad85-c462b4296de9 · outbound

This paper cites Device placement optimization with reinforce- ment learning.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Device placement optimization with reinforce- ment learning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:02.418462Z

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Observation 7e67f924-4cc7-4353-9169-75fe0bf7825b · outbound

This paper cites Pipedream: generalized pipeline parallelism for dnn train- ing.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Pipedream: generalized pipeline parallelism for dnn train- ing

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:02.364659Z

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Observation 380d05f5-8580-4e87-a785-73e8589f9d2d · outbound

This paper cites Sancus: staleness-aware communication-avoiding full- graph decentralized training in large-scale graph neural networks.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Sancus: staleness-aware communication-avoiding full- graph decentralized training in large-scale graph neural networks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:02.329105Z

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Observation 65ed8d03-a91c-4252-b1bc-862013eeeb46 · outbound

This paper cites Fusedmm: A unified sddmm-spmm kernel for graph embedding and graph neural networks.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Fusedmm: A unified sddmm-spmm kernel for graph embedding and graph neural networks

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:02.274773Z

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Observation 65049aef-5c7d-47a0-8808-3a526f13763e · outbound

This paper cites DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale

Reference 52

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This paper cites Zero: Memory optimizations toward training trillion parameter mod- els.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Zero: Memory optimizations toward training trillion parameter mod- els

Reference 53

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This paper cites Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Learn Locally, Correct Globally: A Distributed Algorithm for Training Graph Neural Networks

Reference 54

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Observation 8ee42cb4-310e-4f75-a8ce-ece2d785048e · outbound

This paper cites Deepspeed: System optimizations enable training deep learning mod- els with over 100 billion parameters.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Deepspeed: System optimizations enable training deep learning mod- els with over 100 billion parameters

Reference 55

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This paper cites Algorithms for scheduling independent tasks.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Algorithms for scheduling independent tasks

Reference 56

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Observation 9189f8bc-bdda-46c3-82ba-50abd2636540 · outbound

This paper cites Horovod: fast and easy distributed deep learning in TensorFlow.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Horovod: fast and easy distributed deep learning in TensorFlow

Reference 57

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Observation 8a880ebf-9a11-49ce-8d5a-8122d0ec683e · outbound

This paper cites Mesh-tensorflow: Deep learning for supercomputers.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Mesh-tensorflow: Deep learning for supercomputers

Reference 58

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Observation 40d352c3-29c8-49c0-aaf0-24533ccba5ce · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 59

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Observation f4e393ec-e838-4319-9bfb-d610633b99d3 · outbound

This paper cites Synopsys design compiler.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Synopsys design compiler

Reference 60

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Observation 9cd0c7a5-d439-4097-8b41-4bdc58782e50 · outbound

This paper cites Dorylus: affordable, scalable, and accurate gnn training with distributed cpu servers and serverless threads.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Dorylus: affordable, scalable, and accurate gnn training with distributed cpu servers and serverless threads

Reference 61

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Observation a4d33c43-7177-494e-9fe3-f78a192c8417 · outbound

This paper cites Reducing Communication in Graph Neural Network Training.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Reducing Communication in Graph Neural Network Training

Reference 62

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Observation 7e7b17ad-ad80-418d-8ba4-b88d7e979228 · outbound

This paper cites Graph Attention Networks.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Graph Attention Networks

Reference 63

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Observation a770c2b6-0991-470a-9da3-cf781b35cc45 · outbound

This paper cites Adaptive message quan- tization and parallelization for distributed full-graph gnn training.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Adaptive message quan- tization and parallelization for distributed full-graph gnn training

Reference 64

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Observation f98be4b5-ff80-4c04-8aea-3ea9058a0a02 · outbound

This paper cites BNS- GCN: Efficient full-graph training of graph convolutional networks with partition-parallelism and random boundary node sampling.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators BNS- GCN: Efficient full-graph training of graph convolutional networks with partition-parallelism and random boundary node sampling

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:01.978340Z

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Observation 4587520d-d135-4149-b62d-8d95c9b56882 · outbound

This paper cites Wolfe, Anastasios Kyrillidis, Nam Sung Kim, and Yingyan Lin.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Wolfe, Anastasios Kyrillidis, Nam Sung Kim, and Yingyan Lin

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:01.933270Z

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Observation 1267dc0d-981c-4a75-82f6-b9fe313081cb · outbound

This paper cites Towards Cognitive AI Systems: a Survey and Prospective on Neuro-Symbolic AI.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Towards Cognitive AI Systems: a Survey and Prospective on Neuro-Symbolic AI

Reference 67

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Observation 835b8b77-41cd-4add-90f8-b678680feab1 · outbound

This paper cites Flexgraph: a flexible and efficient distributed framework for gnn training.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Flexgraph: a flexible and efficient distributed framework for gnn training

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:01.890633Z

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Observation ca78c1c0-447d-4f2a-820c-5460288bee04 · outbound

This paper cites Supporting very large models using automatic dataflow graph partitioning.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Supporting very large models using automatic dataflow graph partitioning

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:01.856788Z

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Observation 5d290b2d-c270-4682-a6fe-5bba44dd1631 · outbound

This paper cites Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

Reference 70

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This paper cites Neutronstar: distributed gnn training with hybrid dependency management.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Neutronstar: distributed gnn training with hybrid dependency management

Reference 71

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Observation 6da99ef0-b5b7-497b-981c-529d4695541d · outbound

This paper cites GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs

Reference 72

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Observation 04ef5575-beb6-448a-97d3-5e090e4976d7 · outbound

This paper cites how graph neural networks go beyond weisfeiler-lehman?.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators how graph neural networks go beyond weisfeiler-lehman?

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:01.748058Z

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Observation 58b0993f-a610-4bad-bfd2-354f248a1864 · outbound

This paper cites A comprehensive survey on graph neural networks.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators A comprehensive survey on graph neural networks

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:01.719851Z

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Observation 211a737e-8cfb-4d27-babd-abbddde3e4ca · outbound

This paper cites Graph learning: A survey.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Graph learning: A survey

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:01.683972Z

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Observation 5ae5805c-cbec-452a-9a1d-dfdc7f84c6b4 · outbound

This paper cites How Powerful are Graph Neural Networks?.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators How Powerful are Graph Neural Networks?

Reference 76

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This paper cites GSPMD: General and Scalable Parallelization for ML Computation Graphs.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators GSPMD: General and Scalable Parallelization for ML Computation Graphs

Reference 77

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This paper cites Hygcn: A gcn accelerator with hybrid architecture.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Hygcn: A gcn accelerator with hybrid architecture

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:01.640331Z

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Observation 1676b62e-3f12-4397-8fe0-94122f328542 · outbound

This paper cites Defining and evaluating network com- munities based on ground-truth.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Defining and evaluating network com- munities based on ground-truth

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:01.609457Z

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Observation b7be38e9-e0d2-4528-a1cd-512d6665a03c · outbound

This paper cites Optimal all-to-all personalized exchange in self-routable multistage networks.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Optimal all-to-all personalized exchange in self-routable multistage networks

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:01.585646Z

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Observation 36f83b9b-087d-4ade-a8c3-8a79ad3ff20a · outbound

This paper cites Graph convolutional neural networks for web-scale recommender systems.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Graph convolutional neural networks for web-scale recommender systems

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:01.563709Z

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Observation 5761de6c-1f3a-4b74-9879-388143157dc2 · outbound

This paper cites GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design

Reference 82

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Observation 043d9979-41bb-4e62-8ef3-754470da660e · outbound

This paper cites Graphact: Accelerating gcn training on cpu-fpga heterogeneous platforms.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Graphact: Accelerating gcn training on cpu-fpga heterogeneous platforms

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:01.517837Z

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Observation f71bf75c-46be-4796-9987-846e59d84a8e · outbound

This paper cites Hardware accel- eration of large scale gcn inference.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Hardware accel- eration of large scale gcn inference

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:01.480358Z

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Observation d8bdd40a-449d-4617-b1fa-407f42a8deb7 · outbound

This paper cites Autosync: Learning to synchronize for data-parallel distributed deep learning.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Autosync: Learning to synchronize for data-parallel distributed deep learning

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:01.414892Z

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Observation 226251fb-c1b5-4d19-af6c-c9f5c1cb842f · outbound

This paper cites Understanding gnn computational graph: A coordinated computation, io, and memory perspective.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Understanding gnn computational graph: A coordinated computation, io, and memory perspective

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:01.380663Z

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Observation eef5f213-4459-43c6-8fcf-05e22b254284 · outbound

This paper cites Sylvie: 3d-adaptive and universal system for large-scale graph neural network training.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Sylvie: 3d-adaptive and universal system for large-scale graph neural network training

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:01.337455Z

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Observation 074e3a38-5b1f-415a-87f7-6211cd42e502 · outbound

This paper cites A survey on graph neural network acceleration: Algorithms, systems, and customized hardware.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators A survey on graph neural network acceleration: Algorithms, systems, and customized hardware

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-10T22:20:59.466573Z

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Observation 0017fa5e-48dd-41b9-893c-814409a72f4b · outbound

This paper cites G-cos: Gnn-accelerator co-search towards both better accuracy and efficiency.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators G-cos: Gnn-accelerator co-search towards both better accuracy and efficiency

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:01.295822Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 354e0a3f-1189-409a-8e77-fc09bf6937e8 · outbound

This paper cites PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-10T22:20:59.490966Z

Source-reported events for the cited work

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Observation cfbc0ac6-f3d0-4db7-bab7-145e04795291 · outbound

This paper cites Distdgl: dis- tributed graph neural network training for billion-scale graphs.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Distdgl: dis- tributed graph neural network training for billion-scale graphs

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:21:01.278205Z

Source-reported events for the cited work

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Observation 29e2cab1-2fc9-4a87-ad2f-824e7f23e79e · outbound

This paper cites Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-10T22:20:59.515950Z

Source-reported events for the cited work

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Observation 4e0e9db2-931a-4e59-b5a0-117a1f9dcac7 · outbound

This paper cites AliGraph: A Comprehensive Graph Neural Network Platform.

MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators AliGraph: A Comprehensive Graph Neural Network Platform

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-10T22:20:59.524307Z

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