Scalable Graph Convolutional Network Training on Distributed-Memory Systems

• URL: http://arxiv.org/abs/2212.05009v2
• Date: Tue, 13 Dec 2022 12:21:25 GMT
• Title: Scalable Graph Convolutional Network Training on Distributed-Memory Systems
• Authors: Gunduz Vehbi Demirci, Aparajita Haldar, Hakan Ferhatosmanoglu
• Abstract summary: Graph Convolutional Networks (GCNs) are extensively utilized for deep learning on graphs. Since the convolution operation on graphs induces irregular memory access patterns, designing a memory- and communication-efficient parallel algorithm for GCN training poses unique challenges. We propose a highly parallel training algorithm that scales to large processor counts.
• Score: 5.169989177779801
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Convolutional Networks (GCNs) are extensively utilized for deep learning on graphs. The large data sizes of graphs and their vertex features make scalable training algorithms and distributed memory systems necessary. Since the convolution operation on graphs induces irregular memory access patterns, designing a memory- and communication-efficient parallel algorithm for GCN training poses unique challenges. We propose a highly parallel training algorithm that scales to large processor counts. In our solution, the large adjacency and vertex-feature matrices are partitioned among processors. We exploit the vertex-partitioning of the graph to use non-blocking point-to-point communication operations between processors for better scalability. To further minimize the parallelization overheads, we introduce a sparse matrix partitioning scheme based on a hypergraph partitioning model for full-batch training. We also propose a novel stochastic hypergraph model to encode the expected communication volume in mini-batch training. We show the merits of the hypergraph model, previously unexplored for GCN training, over the standard graph partitioning model which does not accurately encode the communication costs. Experiments performed on real-world graph datasets demonstrate that the proposed algorithms achieve considerable speedups over alternative solutions. The optimizations achieved on communication costs become even more pronounced at high scalability with many processors. The performance benefits are preserved in deeper GCNs having more layers as well as on billion-scale graphs.

Related papers

• Communication-Free Distributed GNN Training with Vertex Cut [63.22674903170953]
  CoFree-GNN is a novel distributed GNN training framework that significantly speeds up the training process by implementing communication-free training. We demonstrate that CoFree-GNN speeds up the GNN training process by up to 10 times over the existing state-of-the-art GNN training approaches.
  arXiv  Detail & Related papers  (2023-08-06T21:04:58Z)
• A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking [124.21408098724551]
  Large-scale graph training is a notoriously challenging problem for graph neural networks (GNNs) We present a new ensembling training manner, named EnGCN, to address the existing issues. Our proposed method has achieved new state-of-the-art (SOTA) performance on large-scale datasets.
  arXiv  Detail & Related papers  (2022-10-14T03:43:05Z)
• Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks [52.566735716983956]
  We propose a graph gradual pruning framework termed CGP to dynamically prune GNNs. Unlike LTH-based methods, the proposed CGP approach requires no re-training, which significantly reduces the computation costs. Our proposed strategy greatly improves both training and inference efficiency while matching or even exceeding the accuracy of existing methods.
  arXiv  Detail & Related papers  (2022-07-18T14:23:31Z)
• Scaling R-GCN Training with Graph Summarization [71.06855946732296]
  Training of Relation Graph Convolutional Networks (R-GCN) does not scale well with the size of the graph. In this work, we experiment with the use of graph summarization techniques to compress the graph. We obtain reasonable results on the AIFB, MUTAG and AM datasets.
  arXiv  Detail & Related papers  (2022-03-05T00:28:43Z)
• BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing [0.0]
  Graph neural networks (GNNs) have extended the success of deep neural networks (DNNs) to non-Euclidean graph data. Existing systems are inefficient to train large graphs with billions of nodes and edges with GPUs. This paper proposes BGL, a distributed GNN training system designed to address the bottlenecks with a few key ideas.
  arXiv  Detail & Related papers  (2021-12-16T00:37:37Z)
• GIST: Distributed Training for Large-Scale Graph Convolutional Networks [18.964079367668262]
  GIST is a hybrid layer and graph sampling method, which disjointly partitions the global model into several, smaller sub-GCNs. This distributed framework improves model performance and significantly decreases wall-clock training time. GIST seeks to enable large-scale GCN experimentation with the goal of bridging the existing gap in scale between graph machine learning and deep learning.
  arXiv  Detail & Related papers  (2021-02-20T19:25:38Z)
• Distributed Training of Graph Convolutional Networks using Subgraph Approximation [72.89940126490715]
  We propose a training strategy that mitigates the lost information across multiple partitions of a graph through a subgraph approximation scheme. The subgraph approximation approach helps the distributed training system converge at single-machine accuracy.
  arXiv  Detail & Related papers  (2020-12-09T09:23:49Z)
• Accurate, Efficient and Scalable Training of Graph Neural Networks [9.569918335816963]
  Graph Neural Networks (GNNs) are powerful deep learning models to generate node embeddings on graphs. It is still challenging to perform training in an efficient and scalable way. We propose a novel parallel training framework that reduces training workload by orders of magnitude compared with state-of-the-art minibatch methods.
  arXiv  Detail & Related papers  (2020-10-05T22:06:23Z)
• Graph Ordering: Towards the Optimal by Learning [69.72656588714155]
  Graph representation learning has achieved a remarkable success in many graph-based applications, such as node classification, prediction, and community detection. However, for some kind of graph applications, such as graph compression and edge partition, it is very hard to reduce them to some graph representation learning tasks. In this paper, we propose to attack the graph ordering problem behind such applications by a novel learning approach.
  arXiv  Detail & Related papers  (2020-01-18T09:14:16Z)
• GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms [1.2183405753834562]
  Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art deep learning model for representation learning on graphs. It is challenging to accelerate training of GCNs due to substantial and irregular data communication. We design a novel accelerator for training GCNs on CPU-FPGA heterogeneous systems.
  arXiv  Detail & Related papers  (2019-12-31T21:19:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.