FeatGraph：一个灵活高效的图神经网络系统后端
FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems
摘要：
图神经网络（GNN）作为图上的机器学习的有前途的方法越来越受欢迎。与每个顶点/边与标量相关联的传统图形工作负载不同，GNN将特征张量附加到每个顶点/边。这个额外的特征维度，以及因此更复杂的顶点和边计算，对局部性和并行性有巨大的影响，现有的图形处理系统无法利用。本文提出了一种新的方法，通过协同优化图遍历和特征维度计算来加速GNN工作负载。RIGGraph提供了一个灵活的编程接口，通过在每个顶点/边上组合粗粒度稀疏模板和细粒度用户定义函数（UDF）来表达不同的GNN模型。RightGraph将对图遍历的优化集成到稀疏模板中，并允许用户使用特征维度计划（FDS）指定对UDF的优化。Graph可将端到端GNN训练和推理的速度在CPU上提高32倍，在GPU上提高7倍。
英文摘要：
Graph neural networks (GNNs) are gaining increasing popularity as a promising approach to machine learning on graphs. Unlike traditional graph workloads where each vertex/edge is associated with a scalar, GNNs attach a feature tensor to each vertex/edge. This additional feature dimension, along with consequently more complex vertex- and edge-wise computations, has enormous implications on locality and parallelism, which existing graph processing systems fail to exploit. This paper proposes FeatGraph to accelerate GNN workloads by co-optimizing graph traversal and feature dimension computation. FeatGraph provides a flexible programming interface to express diverse GNN models by composing coarse-grained sparse templates with fine-grained user-defined functions (UDFs) on each vertex/edge. FeatGraph incorporates optimizations for graph traversal into the sparse templates and allows users to specify optimizations for UDFs with a feature dimension schedule (FDS). FeatGraph speeds up end-to-end GNN training and inference by up to 32x on CPU and 7x on GPU.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
