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UID:submissions.supercomputing.org_SC20_sess177_pap546@linklings.com
SUMMARY:FeatGraph: A Flexible and Efficient Backend for Graph Neural Netwo
 rk Systems
DESCRIPTION:Paper\n\nFeatGraph: A Flexible and Efficient Backend for Graph
  Neural Network Systems\n\nHu, Ye, Wang, Yu, Zheng...\n\nGraph neural netw
 orks (GNNs) are gaining increasing popularity as a promising approach to m
 achine learning on graphs. Unlike traditional graph workloads where each v
 ertex/edge is associated with a scalar, GNNs attach a feature tensor to ea
 ch vertex/edge. This additional feature dimension, along with consequently
  more complex vertex- and edge-wise computations, has enormous implication
 s on locality and parallelism, which existing graph processing systems fai
 l to exploit.\n\nThis paper proposes FeatGraph to accelerate GNN workloads
  by co-optimizing graph traversal and feature dimension computation. FeatG
 raph provides a flexible programming interface to express diverse GNN mode
 ls by composing coarse-grained sparse templates with fine-grained user-def
 ined functions (UDFs) on each vertex/edge. FeatGraph incorporates optimiza
 tions for graph traversal into the sparse templates and allows users to sp
 ecify optimizations for UDFs with a feature dimension schedule (FDS). Feat
 Graph speeds up end-to-end GNN training and inference by up to 32x on CPU 
 and 7x on GPU.\n\nTag: Accelerators, FPGA, and GPUs, Linear Algebra, Machi
 ne Learning, Deep Learning and Artificial Intelligence\n\nRegistration Cat
 egory: Tech Program Reg Pass
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