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DTSTART;TZID=America/New_York:20241118T163800
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UID:submissions.supercomputing.org_SC24_sess761_ws_hppss103@linklings.com
SUMMARY:Accelerating Python Applications with Dask and ProxyStore
DESCRIPTION:J. Gregory Pauloski (University of Chicago, Argonne National L
 aboratory (ANL)); Klaudiusz Rydzy (Loyola University, Chicago); and Valeri
 e Hayot-Sasson, Ian Foster, and Kyle Chard (University of Chicago, Argonne
  National Laboratory (ANL))\n\nApplications are increasingly written as dy
 namic workflows underpinned by an execution framework that manages asynchr
 onous computations across distributed hardware. However, execution framewo
 rks typically offer one-size-fits-all solutions for data flow management, 
 which can restrict performance and scalability. ProxyStore, a middleware l
 ayer that optimizes data flow via an advanced pass-by-reference paradigm, 
 has shown to be an effective mechanism for addressing these limitations. H
 ere, we investigate integrating ProxyStore with Dask Distributed, one of t
 he most popular libraries for distributed computing in Python, with the go
 al of supporting scalable and portable scientific workflows. Dask provides
  an easy-to-use and flexible framework, but is less optimized for scaling 
 certain data-intensive workflows. We investigate these limitations and det
 ail the technical contributions necessary to develop a robust solution for
  distributed applications and demonstrate improved performance on syntheti
 c benchmarks and real applications.\n\nTag: Applications and Application F
 rameworks, Artificial Intelligence/Machine Learning, Parallel Programming 
 Methods, Models, Languages and Environments\n\nRegistration Category: Work
 shop Reg Pass\n\nSession Chairs: Sunita Chandrasekaran (University of Dela
 ware), Sam Foreman (Argonne National Laboratory (ANL)), Daniel Margala (La
 wrence Berkeley National Laboratory (LBNL)), and Pete Mendygral (Hewlett P
 ackard Enterprise (HPE))\n\n
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