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DTSTAMP:20210402T160556Z
LOCATION:Track 9
DTSTART;TZID=America/New_York:20201112T121000
DTEND;TZID=America/New_York:20201112T123500
UID:submissions.supercomputing.org_SC20_sess215_ws_canopie103@linklings.co
 m
SUMMARY:Enabling Seamless Execution of Computational and Data Science Work
 flows on HPC and Cloud with the Popper Container-Native Automation Engine
DESCRIPTION:Workshop\n\nEnabling Seamless Execution of Computational and D
 ata Science Workflows on HPC and Cloud with the Popper Container-Native Au
 tomation Engine\n\nChakraborty, Maltzahn, Jimenez\n\nResearchers working i
 n various fields of computational science often find it difficult to repro
 duce experiments from artifacts like code, data, diagrams and results whic
 h are left behind by the previous researchers. The code developed on one m
 achine often fails to run on other machines due to differences in hardware
  architecture, OS and software dependencies, among others. This is accompa
 nied by the difficulty in understanding how artifacts are organized, as we
 ll as in using them in the correct order. Software containers can be used 
 to address some of these problems, and thus researchers and developers hav
 e built scientific workflow engines that execute the steps of a workflow i
 n separate containers. Existing container-native workflow engines assume t
 he availability of infrastructure deployed in the cloud or HPC centers. In
  this paper, we present Popper, a container-native workflow engine that do
 es not assume the presence of a Kubernetes cluster or any service other th
 an a container engine such as Docker or Podman. We introduce the design an
 d architecture of Popper and describe how it abstracts away the complexity
  of multiple container engines and resource managers, enabling users to fo
 cus only on writing workflow logic. With Popper, researchers can build and
  validate workflows easily in almost any environment of their choice inclu
 ding local machines, SLURM-based HPC clusters, CI services or Kubernetes-b
 ased cloud computing environments. To exemplify the suitability of this wo
 rkflow engine, we present three case studies in which we take examples fro
 m machine learning and high-performance computing and turn them into Poppe
 r workflows.\n\nRegistration Category: Workshop Reg Pass
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