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DTSTART;TZID=America/Denver:20231113T102500
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UID:submissions.supercomputing.org_SC23_sess450_ws_worksp105@linklings.com
SUMMARY:TaskVine: Managing In-Cluster Storage for High-Throughput Data Int
 ensive Workflows
DESCRIPTION:Barry Sly-Delgado, Thanh Son Phung, Colin Thomas, David Simone
 tti, Andrew Hennessee, Ben Tovar, and Douglas Thain (University of Notre D
 ame)\n\nMany scientific applications are expressed as high-throughput work
 flows that consist of large graphs of data assets and tasks to be executed
  on large parallel and distributed systems. A challenge in executing these
  workflows is managing data: both datasets and software must be efficientl
 y distributed to cluster nodes; intermediate data must be conveyed between
  tasks; output data must be delivered to its destination. Scaling problems
  result when these actions are performed in an uncoordinated manner on a s
 hared filesystem. To address this problem, we introduce TaskVine: a system
  for exploiting the aggregate local storage and network capacity of a larg
 e cluster. TaskVine tracks the lifetime of data in a workflow --from archi
 val sources to final outputs-- making use of local storage to distribute a
 nd re-use data. We describe the architecture and novel capabilities of Tas
 kVine, and demonstrate its use with applications in genomics, high energy 
 physics, molecular dynamics, and machine learning.\n\nTag: Data Analysis, 
 Visualization, and Storage, Large Scale Systems, Programming Frameworks an
 d System Software, Reproducibility, Resource Management, Runtime Systems\n
 \nRegistration Category: Workshop Reg Pass\n\nSession Chairs: Silvina Cain
 o-Lores (National Institute for Research in Digital Science and Technology
  (Inria)) and Anirban Mandal (Renaissance Computing Institute (RENCI), Uni
 versity of North Carolina at Chapel Hill)\n\n
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