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UID:submissions.supercomputing.org_SC23_sess436_ws_ia103@linklings.com
SUMMARY:cuAlign: Scalable Network Alignment on GPU Accelerators
DESCRIPTION:Workshop\n\nLizhi Xiang (University of Utah), Arif Khan (Meta)
 , S. M. Ferdous (Pacific Northwest National Laboratory (PNNL)), SR Aravind
  (Meta), and Mahantesh Halappanavar (Pacific Northwest National Laboratory
  (PNNL))\n\nGiven two graphs, the objective of network alignment is to fin
 d a one-to-one mapping of vertices in one graph to vertices in the other, 
 such that the number of overlaps is maximized. Network alignment is an imp
 ortant optimization problem with several applications in bioinformatics, c
 omputer vision and ontology matching. Since it is an NP-hard problem, effi
 cient heuristics and scalable implementations are necessary. In this work,
  we introduce a novel framework (cuAlign) that combines intra-network prox
 imity using node (vertex) embedding, sparsification for computational effi
 ciency, and belief propagation (BP) and approximate weighted matching for 
 alignment. We demonstrate qualitative improvements up to 22% over state-of
 -the-art approaches and provide a scalable implementation targeting modern
  GPU accelerators. We demonstrate up to 19× speedup for belief propagation
 , 3× speedup for approximate weighted matching, and 15× total, relative to
  a state-of-the-art multi-threaded implementation. We believe that our wor
 k will enable algorithmic improvements and applications of network alignme
 nt.\n\nTag: Algorithms, Applications, Architecture and Networks\n\nRegistr
 ation Category: Workshop Reg Pass\n\nSession Chairs: Sanjukta Bhowmick (Un
 iversity of North Texas), Vito Giovanni Castellana (Pacific Northwest Nati
 onal Laboratory (PNNL)), John Feo (Pacific Northwest National Laboratory (
 PNNL)), Marco Minutoli (Pacific Northwest National Laboratory (PNNL)), and
  Antonino Tumeo (Pacific Northwest National Laboratory (PNNL))
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