We propose a new framework for computing the embeddings of large-scale graphs on a single machine. A graph embedding is a fixed length vector representation for each node (and/or edge-type) in a graph and has emerged as the de-facto approach to apply modern machine learning on graphs. We identify that current systems for learning the embeddings of large-scale graphs are bottlenecked by data movement, which results in poor resource utilization and inefficient training. These limitations require state-of-the-art systems to distribute training across multiple machines. We propose Gaius, a system for efficient training of graph embeddings that leverages partition caching and buffer-aware data orderings to minimize disk access and interleaves data movement with computation to maximize utilization. We compare Gaius against two state-of-the-art industrial systems on a diverse array of benchmarks. We demonstrate that Gaius achieves the same level of accuracy but is up to one order-of magnitude faster. We also show that Gaius can scale training to datasets an order of magnitude beyond a single machine's GPU and CPU memory capacity, enabling training of configurations with more than a billion edges and 550GB of total parameters on a single AWS P3.2xLarge instance.

提出了一种名为 Marius 的系统，用于在单台机器上训练大规模图的嵌入向量，利用分区缓存和缓冲区感知数据排序减少磁盘访问和优化数据移动，并在多种基准测试中表现出比现有工业系统高十倍的效率和可扩展性。

Marius: 在单机上学习大规模图嵌入