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DTSTART;TZID=America/Denver:20231116T160000
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UID:submissions.supercomputing.org_SC23_sess163_pap178@linklings.com
SUMMARY:Choosing the Best Parallelization and Implementation Styles for Gr
 aph Analytics Codes: Lessons Learned from 1106 Programs
DESCRIPTION:Yiqian Liu, Noushin Azami, Avery VanAusdal, and Martin Burtsch
 er (Texas State University)\n\nGraph analytics has become a major workload
  in recent years. The underlying core algorithms tend to be irregular and 
 data dependent, making them challenging to parallelize. Yet, these algorit
 hms can be implemented and parallelized in many ways for CPUs and even mor
 e ways for GPUs. We took 6 key graph algorithms and created hundreds of pa
 rallel CUDA, OpenMP, and parallel C++ versions of each of them, most of wh
 ich have never been described or studied. To determine which parallelizati
 on and implementation styles work well and under what circumstances, we ev
 aluated the resulting 1106 programs on 2 GPUs and 2 CPUs using 5 input gra
 phs. Our results show which styles and combinations thereof work well and 
 which ones should be avoided. We found that choosing the wrong implementat
 ion style can yield over a 10x performance loss on average. The worst comb
 inations of styles can cost 6 orders of magnitude in performance.\n\nTag: 
 Architecture and Networks, Data Movement and Memory, Graph Algorithms and 
 Frameworks, Performance Measurement, Modeling, and Tools, Programming Fram
 eworks and System Software\n\nRegistration Category: Tech Program Reg Pass
 \n\nReproducibility Badges: Artifact Available, Artifact Functional, Resul
 ts Reproduced\n\nSession Chair: Mahantesh Halappanavar (Pacific Northwest 
 National Laboratory (PNNL))\n\n
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