Boosting Graph Embedding on a Single GPU

• URL: http://arxiv.org/abs/2110.10049v1
• Date: Tue, 19 Oct 2021 15:25:04 GMT
• Title: Boosting Graph Embedding on a Single GPU
• Authors: Amro Alabsi Aljundi, Taha Atahan Aky{\i}ld{\i}z, Kamer Kaya
• Abstract summary: We present GOSH, a GPU-based tool for embedding large-scale graphs with minimum hardware constraints. It 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.
• Score: 3.093890460224435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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-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.

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