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DTSTAMP:20260422T000630Z
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DTSTART;TZID=America/Denver:20231116T153000
DTEND;TZID=America/Denver:20231116T170000
UID:submissions.supercomputing.org_SC23_sess163@linklings.com
SUMMARY:Graph Analytics
DESCRIPTION:Choosing the Best Parallelization and Implementation Styles fo
 r Graph Analytics Codes: Lessons Learned from 1106 Programs\n\nGraph analy
 tics has become a major workload in recent years. The underlying core algo
 rithms tend to be irregular and data dependent, making them challenging to
  parallelize. Yet, these algorithms can be implemented and parallelized in
  many ways for CPUs and even more ways for GPUs. We took 6 key grap...\n\n
 \nYiqian Liu, Noushin Azami, Avery VanAusdal, and Martin Burtscher (Texas 
 State University)\n---------------------\nDGAP: Efficient Dynamic Graph An
 alysis on Persistent Memory\n\nDynamic graphs have grown in importance for
  numerous real-world applications. To accommodate this, graph frameworks, 
 particularly their internal data structures, must support both persistent 
 graph updates and rapid graph analysis simultaneously. Emerging persistent
  memory technologies, such as Optan...\n\n\nAbdullah Al Raqibul Islam and 
 Dong Dai (University of North Carolina, Charlotte)\n---------------------\
 nPhases, Modalities, Spatial and Temporal Locality: Domain Specific ML Pre
 fetcher for Accelerating Graph Analytics\n\nMemory performance is a bottle
 neck in graph analytics acceleration. Existing Machine Learning (ML) prefe
 tchers struggle with phase transitions and irregular memory accesses in gr
 aph processing. We propose MPGraph, an ML-based Prefetcher for Graph analy
 tics using domain specific models. MPGraph intro...\n\n\nPengmiao Zhang (U
 niversity of Southern California (USC)), Rajgopal Kannan (DEVCOM US Army R
 esearch Lab), and Viktor K. Prasanna (University of Southern California (U
 SC))\n\nTag: Architecture and Networks, Data Movement and Memory, Graph Al
 gorithms and Frameworks, Performance Measurement, Modeling, and Tools, Pro
 gramming Frameworks and System Software\n\nRegistration Category: Tech Pro
 gram Reg Pass\n\nReproducibility Badges: Artifact Available, Artifact Func
 tional, Results Reproduced\n\nSession Chair: Mahantesh Halappanavar (Pacif
 ic Northwest National Laboratory (PNNL))
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