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DTSTAMP:20181221T160726Z
LOCATION:D173
DTSTART;TZID=America/Chicago:20181111T153000
DTEND;TZID=America/Chicago:20181111T155500
UID:submissions.supercomputing.org_SC18_sess163_ws_works109@linklings.com
SUMMARY:DagOn*: Executing Direct Acyclic Graphs as Parallel Jobs on Anythi
 ng
DESCRIPTION:Workshop\nReproducibility, Scientific Computing, Scientific Wo
 rkflows, Workflows, Workshop Reg Pass, HPC, Data Intensive\n\nDagOn*: Exec
 uting Direct Acyclic Graphs as Parallel Jobs on Anything\n\nMontella, Di L
 uccio, Kosta\n\nThe democratization of computational resources, thanks to 
 the advent of public, private, and hybrid clouds, changed the rules in man
 y science fields. For decades, one of the effort of computer scientists an
 d computer engineers was the development of tools able to simplify access 
 to high-end computational resources by computational scientists. However, 
 nowadays any science field can be considered “computational” as the availa
 bility of powerful, but easy to manage workflow engines, is crucial. In th
 is work, we present DagOn* (Direct acyclic graph On anything), a lightweig
 ht Python library implementing a workflow engine able to execute parallel 
 jobs represented by direct acyclic graphs on any combination of local mach
 ines, on-premise high performance computing clusters, containers, and clou
 d-based virtual infrastructures. We use a real-world production-level appl
 ication for weather and marine forecasts to illustrate the use of this new
  workflow engine.
URL:https://sc18.supercomputing.org/presentation/?id=ws_works109&sess=sess
 163
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