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UID:submissions.supercomputing.org_SC24_sess533_post242@linklings.com
SUMMARY:Seesaw: Elastic Scaling for Task-Based Distributed Programs
DESCRIPTION:Matthew Chung (University of California, Riverside)\n\nModern 
 batch schedulers in HPC environments enable the shared use of available co
 mputational resources via provisioning discrete sets of resources matching
  user requirements. The lack of elasticity in such scenarios is often addr
 essed using a Pilot job model where multiple separate requests are pooled.
  In this work, we explore computational elasticity in a popular Python-bas
 ed workflow system: Parsl. We identify limitations in existing scaling log
 ic and propose a new resource-aware scheduler. We show a significant impro
 vement in the efficiency of compute resources consumed with minimal loss i
 n time to solution.\n\nRegistration Category: Tech Program Reg Pass, Exhib
 its Reg Pass\n\nSession Chairs: Ayesha Afzal (Friedrich-Alexander Universi
 ty, Erlangen-Nuremberg; Erlangen National High Performance Computing Cente
 r); Sally Ellingson (University of Kentucky); and Alan Sussman (University
  of Maryland)\n\n
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