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      FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems

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          Abstract

          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.

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          Author and article information

          Journal
          25 August 2020
          Article
          2008.11359
          b6f9ae7f-8ac8-4c60-8dda-bfbff1657e6c

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          Custom metadata
          To appear in International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'20)
          cs.LG cs.DC

          Networking & Internet architecture,Artificial intelligence
          Networking & Internet architecture, Artificial intelligence

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