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UID:submissions.supercomputing.org_SC23_sess291_rpost157@linklings.com
SUMMARY:Minimizing Data Movement Using Distant Futures
DESCRIPTION:Barry Sly-Delgado and Douglas Thain (University of Notre Dame)
 \n\nScientific workflows execute a series of tasks where each task may con
 sume data as an input and produce data as an output. Within these workflow
 s, tasks often produce intermediate results that may serve as inputs to su
 bsequent tasks within the workflow. These results can vary in size and may
  need to be transported to another worker node. Data movement can become t
 he primary bottleneck for many scientific workflows thus minimizing the co
 st of data movement can provide a significant performance benefit for a gi
 ven workflow. Distant futures enable transfers between worker nodes, elimi
 nating the need for intermediate results to pass through a centralized man
 ager for future tasks invocations. Additionally, asynchronous transfers en
 able increased concurrency by preventing the blocking of task invocations.
  This poster shows the performance benefit received from the implementatio
 n of distant futures within a workflow that produces numerous intermediate
  results.\n\nTag: Artificial Intelligence/Machine Learning, Architecture a
 nd Networks, Heterogeneous Computing, I/O and File Systems, Performance Me
 asurement, Modeling, and Tools, Post-Moore Computing, Programming Framewor
 ks and System Software, Quantum Computing\n\nRegistration Category: Tech P
 rogram Reg Pass, Exhibits Reg Pass\n\n
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