{"url":"/method/distdgl","slug":"distdgl","name":"DistDGL","full_name":"DistDGL","full_name_withheld":false,"description_markdown":"**DistDGL** is a system for training GNNs in a mini-batch fashion on a cluster of machines. It is is based on the Deep Graph Library (DGL), a popular GNN development framework. DistDGL distributes the graph and its associated data (initial features and embeddings) across the machines and uses this distribution to derive a computational decomposition by following an owner-compute rule. DistDGL follows a synchronous training approach and allows ego-networks forming the mini-batches to include non-local nodes. To minimize the overheads associated with distributed computations, DistDGL uses a high-quality and light-weight mincut graph partitioning algorithm along with multiple balancing constraints. This allows it to reduce communication overheads and statically balance the computations. It further reduces the communication by replicating halo nodes and by using sparse embedding updates. The combination of these design choices allows DistDGL to train high-quality models while achieving high parallel efficiency and memory scalability","description_state":"present","introduced_year":null,"introduced_by":{"title":"DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs","paper":"/paper/distdgl-distributed-graph-neural-network","first_author":"Da Zheng","n_authors":9,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/distdgl-distributed-graph-neural-network"},"source":{"url":"https://arxiv.org/abs/2010.05337v3","title":"DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"Distributed Methods","url":"/methods/category/distributed-methods","pwc_aliases":[]}],"n_papers_tagged":7,"archive_num_papers":7,"papers_newest_first":[{"paper":"/paper/massivegnn-efficient-training-via-prefetching","title":"MassiveGNN: Efficient Training via Prefetching for Massively Connected Distributed Graphs","date":"2024-10-30","arxiv_id":"2410.22697","n_code_links":1,"syntology":null},{"paper":"/paper/entropy-aware-training-for-fast-and-accurate","title":"Entropy Aware Training for Fast and Accurate Distributed GNN","date":"2023-11-04","arxiv_id":"2311.02399","n_code_links":1,"syntology":null},{"paper":null,"title":"BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphs","date":"2023-06-23","arxiv_id":"2306.13814","n_code_links":0,"syntology":null},{"paper":"/paper/communication-efficient-graph-neural-networks","title":"Communication-Efficient Graph Neural Networks with Probabilistic Neighborhood Expansion Analysis and Caching","date":"2023-05-04","arxiv_id":"2305.03152","n_code_links":2,"syntology":null},{"paper":"/paper/distributed-graph-embedding-with-information","title":"Distributed Graph Embedding with Information-Oriented Random Walks","date":"2023-03-28","arxiv_id":"2303.15702","n_code_links":1,"syntology":null},{"paper":null,"title":"DistGNN-MB: Distributed Large-Scale Graph Neural Network Training on x86 via Minibatch Sampling","date":"2022-11-11","arxiv_id":"2211.06385","n_code_links":0,"syntology":null},{"paper":"/paper/distdgl-distributed-graph-neural-network","title":"DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs","date":"2020-10-11","arxiv_id":"2010.05337","n_code_links":1,"syntology":null}],"papers_shown":7,"tasks":[{"task":"/task/graph-partitioning","name":"graph partitioning","papers":3},{"task":null,"name":"CPU","papers":2},{"task":null,"name":"GPU","papers":2},{"task":"/task/graph-neural-network","name":"Graph Neural Network","papers":2},{"task":"/task/fraud-detection","name":"Fraud Detection","papers":1},{"task":"/task/graph-embedding","name":"Graph Embedding","papers":1},{"task":"/task/link-prediction","name":"Link Prediction","papers":1},{"task":"/task/recommendation-systems","name":"Recommendation Systems","papers":1},{"task":"/task/representation-learning","name":"Representation Learning","papers":1}],"tasks_shown":9,"n_tasks":9,"usage_by_year":[{"year":"2020","papers":1},{"year":"2022","papers":1},{"year":"2023","papers":4},{"year":"2024","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/distdgl"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}