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Boosting Graph Embedding on a Single GPU.
- Source :
-
IEEE Transactions on Parallel & Distributed Systems . No2022, Vol. 33 Issue 11, p3092-3105. 14p. - Publication Year :
- 2022
-
Abstract
- Graphs are ubiquitous, and they can model unique characteristics and complex relations of real-life systems. Although using machine learning (ML) on graphs is promising, their raw representation is not suitable for ML algorithms. Graph embedding represents each node of a graph as a $d$ d -dimensional vector which is more suitable for ML tasks. However, the embedding process is expensive, and CPU-based tools do not scale to real-world graphs. In this work, we present GOSH, a GPU-based tool for embedding large-scale graphs with minimum hardware constraints. GOSH employs a novel graph coarsening algorithm to enhance the impact of updates and minimize the work for embedding. It also incorporates a decomposition schema that enables any arbitrarily large graph to be embedded with a single GPU. As a result, GOSH sets a new state-of-the-art in link prediction both in accuracy and speed, and delivers high-quality embeddings for node classification at a fraction of the time compared to the state-of-the-art. For instance, it can embed a graph with over 65 million vertices and 1.8 billion edges in less than 30 minutes on a single GPU. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10459219
- Volume :
- 33
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Parallel & Distributed Systems
- Publication Type :
- Academic Journal
- Accession number :
- 157073358
- Full Text :
- https://doi.org/10.1109/TPDS.2021.3129617