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UID:submissions.supercomputing.org_SC22_sess226_spostg108@linklings.com
SUMMARY:High Performance Computing Scheduler Parameter Optimization Using 
 Simulation and Regression
DESCRIPTION:ACM Student Research Competition: Graduate Poster, ACM Student
  Research Competition: Undergraduate Poster, Posters\n\nHigh Performance C
 omputing Scheduler Parameter Optimization Using Simulation and Regression\
 n\nHutchison\n\nJobs for a High Performance Computing cluster are allocate
 d system resources by a scheduling application such as SLURM. These schedu
 ling applications are highly configurable by HPC administrators through th
 e use of parameters which modify and customize their scheduling behavior. 
 Although there are default values for these scheduling parameters provided
  by their creators and maintainers, it is unclear which values for schedul
 er parameter settings would be optimal for a particular HPC system running
  the types of jobs its users typically submit. Using over 37,000 jobs from
  historic job log data from Kansas State University’s High Performance Com
 puting cluster, this research uses a SLURM simulator to execute over 90,00
 0 scheduler simulations requiring over 840,000 compute hours along with gr
 adient boosted tree regression to predict an optimal set of scheduler conf
 iguration parameters which results in a 79% decrease in the average job qu
 eue time when compared with the default scheduler parameters\n\nRegistrati
 on Category: Tech Program Reg Pass
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