平行和分布式图形神经网络:深入的同步分析
IEEE transactions on pattern analysis and machine intelligence
|February 22, 2024
概括
本研究介绍了分析图形神经网络 (GNN) 并行性的一种分类法,这对于优化它们在现代硬件上的性能至关重要. 该研究提供了提高GNN效率的见解,并指导未来异步并行GNN管道开发.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 图形神经网络 (GNN) 是复杂网络问题的强大深度学习工具.
- 由于其独特的计算需求,GNN在并行架构上的高效执行面临着挑战.
研究的目的:
- 为GNN并行性设计一个分类学 (数据,模型,管道).
- 分析GNN模型,任务,软件和硬件中的并行性.
- 为最大限度地提高GNN性能提供见解,并确定未来的研究方向.
主要方法:
- 在GNN中开发了平行关系的分类学.
- 利用工作深度模型来评估并行性,通信和同步性.
- 对矢量化进行分析的张量稀疏度/密度.
- 正式分析了GNN管道和通用的消息传递模型.
- 研究了异步平行GNN管道.
主要成果:
- 在各种GNN模型和框架中量化并行性.
- 确定了影响平行架构GNN性能的关键因素.
- 为更深层次的优化提供通用GNN管道.
- 探索未来平行GNN的异步性.
结论:
- 该分类学为理解和优化GNN并行性提供了一个框架.
- 获得的洞察力可以显著提高GNN计算效率.
- 这项工作为先进的异步并行GNN设计奠定了基础.
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