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DTSTART;TZID=America/Denver:20231113T155400
DTEND;TZID=America/Denver:20231113T161800
UID:submissions.supercomputing.org_SC23_sess457_ws_mlg102@linklings.com
SUMMARY:An Efficient Distributed Graph Engine for Deep Learning on Graphs
DESCRIPTION:Gangda Deng, Ömer Akgül, and Hongkuan Zhou (University of Sout
 hern California (USC)); Hanqing Zeng, Yinglong Xia, and Jianbo Li (Meta); 
 and Viktor Prasanna (University of Southern California (USC))\n\nTradition
 al graph-processing algorithms have been widely used in Graph Neural Netwo
 rks (GNNs). Current approaches to graph processing in deep learning face t
 wo main problems. Firstly, easy-to-use deep learning libraries lack suppor
 t for widely used graph processing algorithms and do not provide low-level
  APIs for building distributed graph processing algorithms. Secondly, exis
 ting graph processing libraries are not user-friendly for deep learning re
 searchers. This paper presents an efficient and easy-to-use graph engine t
 hat incorporates distributed graph processing into deep-learning ecosystem
 s.  We develop a distributed graph storage system with an efficient batchi
 ng technique to minimize communication overhead incurred by Remote Procedu
 re Calls between computing nodes. We propose an optimized method for distr
 ibuted computation of Single Source Personalized PageRank (SSPPR) using th
 e Forward Push algorithm based on lock-free parallel maps. Experimental ev
 aluations demonstrate significant improvement, with up to three orders of 
 magnitude in SSPPR throughput, of our graph engine compared with the tenso
 r-based implementation.\n\nTag: Artificial Intelligence/Machine Learning, 
 Graph Algorithms and Frameworks\n\nRegistration Category: Workshop Reg Pas
 s\n\nSession Chairs: Seung-Hwan Lim (Oak Ridge National Laboratory (ORNL))
 ; José Moreira (IBM); Catherine Schuman (University of Tennessee, Knoxvill
 e); and Richard Vuduc (Georgia Institute of Technology)\n\n
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