Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis

• URL: http://arxiv.org/abs/2205.09702v7
• Date: Thu, 17 Aug 2023 20:28:36 GMT
• Title: Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis
• Authors: Maciej Besta, Torsten Hoefler
• Abstract summary: Graph neural networks (GNNs) are among the most powerful tools in deep learning. They routinely solve complex problems on unstructured networks, such as node classification, graph classification, or link prediction, with high accuracy. However, both inference and training of GNNs are complex, and they uniquely combine the features of irregular graph processing with dense and regular computations. This complexity makes it very challenging to execute GNNs efficiently on modern massively parallel architectures.
• Score: 28.464210819376593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph neural networks (GNNs) are among the most powerful tools in deep learning. They routinely solve complex problems on unstructured networks, such as node classification, graph classification, or link prediction, with high accuracy. However, both inference and training of GNNs are complex, and they uniquely combine the features of irregular graph processing with dense and regular computations. This complexity makes it very challenging to execute GNNs efficiently on modern massively parallel architectures. To alleviate this, we first design a taxonomy of parallelism in GNNs, considering data and model parallelism, and different forms of pipelining. Then, we use this taxonomy to investigate the amount of parallelism in numerous GNN models, GNN-driven machine learning tasks, software frameworks, or hardware accelerators. We use the work-depth model, and we also assess communication volume and synchronization. We specifically focus on the sparsity/density of the associated tensors, in order to understand how to effectively apply techniques such as vectorization. We also formally analyze GNN pipelining, and we generalize the established Message-Passing class of GNN models to cover arbitrary pipeline depths, facilitating future optimizations. Finally, we investigate different forms of asynchronicity, navigating the path for future asynchronous parallel GNN pipelines. The outcomes of our analysis are synthesized in a set of insights that help to maximize GNN performance, and a comprehensive list of challenges and opportunities for further research into efficient GNN computations. Our work will help to advance the design of future GNNs.

Related papers

• LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework [30.54068909225463]
  We aim to streamline the GNN design process and leverage the advantages of Large Language Models (LLMs) to improve the performance of GNNs on downstream tasks. We formulate a new paradigm, coined "LLMs-as-Consultants," which integrates LLMs with GNNs in an interactive manner. We empirically evaluate the effectiveness of LOGIN on node classification tasks across both homophilic and heterophilic graphs.
  arXiv  Detail & Related papers  (2024-05-22T18:17:20Z)
• GNNPipe: Scaling Deep GNN Training with Pipelined Model Parallelism [10.723541176359452]
  Communication is a key bottleneck for distributed graph neural network (GNN) training. GNNPipe is a new approach that scales the distributed full-graph deep GNN training.
  arXiv  Detail & Related papers  (2023-08-19T18:44:14Z)
• Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication [100.51884192970499]
  GNNs are a powerful family of neural networks for learning over graphs. scaling GNNs either by deepening or widening suffers from prevalent issues of unhealthy gradients, over-smoothening, information squashing. We propose not to deepen or widen current GNNs, but instead present a data-centric perspective of model soups tailored for GNNs.
  arXiv  Detail & Related papers  (2023-06-18T03:33:46Z)
• GNN-Ensemble: Towards Random Decision Graph Neural Networks [3.7620848582312405]
  Graph Neural Networks (GNNs) have enjoyed wide spread applications in graph-structured data. GNNs are required to learn latent patterns from a limited amount of training data to perform inferences on a vast amount of test data. In this paper, we push one step forward on the ensemble learning of GNNs with improved accuracy, robustness, and adversarial attacks.
  arXiv  Detail & Related papers  (2023-03-20T18:24:01Z)
• Distributed Graph Neural Network Training: A Survey [51.77035975191926]
  Graph neural networks (GNNs) are a type of deep learning models that are trained on graphs and have been successfully applied in various domains. Despite the effectiveness of GNNs, it is still challenging for GNNs to efficiently scale to large graphs. As a remedy, distributed computing becomes a promising solution of training large-scale GNNs.
  arXiv  Detail & Related papers  (2022-11-01T01:57:00Z)
• 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)
• Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis [94.64007376939735]
  We theoretically characterize the impact of connectivity patterns on the convergence of deep neural networks (DNNs) under gradient descent training. We show that by a simple filtration on "unpromising" connectivity patterns, we can trim down the number of models to evaluate.
  arXiv  Detail & Related papers  (2022-05-11T17:43:54Z)
• Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth [57.10183643449905]
  Graph Neural Networks (GNNs) have been studied from the lens of expressive power and generalization. We study the dynamics of GNNs by studying deep skip optimization. Our results provide first theoretical support for the success of GNNs.
  arXiv  Detail & Related papers  (2021-05-10T17:59:01Z)
• BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices [9.406007544032848]
  Graph Neural Networks (GNNs) are state-of-the-art algorithms for analyzing non-euclidean graph data. How to inference GNNs in real time has become a challenging problem for some resource-limited edge-computing platforms. We propose BlockGNN, a software- hardware co-design approach to realize efficient GNN acceleration.
  arXiv  Detail & Related papers  (2021-04-13T14:09:22Z)
• Analyzing the Performance of Graph Neural Networks with Pipe Parallelism [2.269587850533721]
  We focus on Graph Neural Networks (GNNs) that have found great success in tasks such as node or edge classification and link prediction. New approaches for processing larger networks are needed to advance graph techniques. We study how GNNs could be parallelized using existing tools and frameworks that are known to be successful in the deep learning community.
  arXiv  Detail & Related papers  (2020-12-20T04:20:38Z)
• Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks [55.98291376393561]
  Graph neural networks (GNNs) have emerged as a powerful tool for learning software engineering tasks. Recurrent neural networks (RNNs) are well-suited to long sequential chains of reasoning, but they do not naturally incorporate program structure. We introduce a novel GNN architecture, the Instruction Pointer Attention Graph Neural Networks (IPA-GNN), which improves systematic generalization on the task of learning to execute programs.
  arXiv  Detail & Related papers  (2020-10-23T19:12:30Z)

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.