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DTSTAMP:20211207T055414Z
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DTSTART;TZID=America/Chicago:20211117T103000
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UID:submissions.supercomputing.org_SC21_sess168@linklings.com
SUMMARY:Distributed Training and Graphs
DESCRIPTION:Paper\n\nElGA: Elastic and Scalable Dynamic Graph Analysis\n\n
 Gabert, Sancak, Ozkaya, Pinar, Catalyurek\n\nModern graphs are not only la
 rge, but rapidly changing.  The rate of change can vary significantly alon
 g with the computational cost.  Existing distributed graph analysis system
 s have largely been designed to operate on static graphs.  Infrastructure 
 changes in these systems need to occur when the s...\n\n------------------
 ---\nKrill: A Compiler and Runtime System for Concurrent Graph Processing\
 n\nChen, Shen, Xiao, Lu\n\nAs a large number of emerging graph application
 s spread across different domains, the need for processing massive concurr
 ent graph jobs (CGJs) is increasing. However, existing graph processing sy
 stems designed for a single job cannot efficiently tackle multiple CGJs, w
 here they suffer from interferi...\n\n---------------------\nDistributed M
 ultigrid Neural Solver on Megavoxel Domains\n\nBalu, Botelho, Khara, Rao, 
 Sarkar...\n\nWe consider the distributed training of neural networks that 
 serve as PDE solvers producing full field outputs. We specifically conside
 r neural solvers for the generalized 3D Poisson equation over megavoxel do
 mains. A scalable framework is presented that integrates two distinct adva
 nces. First, we a...\n\n\nTag: Machine Learning and Artificial Intelligenc
 e, Numerical Algorithms\n\nRegistration Category: Tech Program Reg Pass
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