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@INPROCEEDINGS{Hoffstaedter:1025704,
      author       = {Hoffstaedter, Felix},
      title        = {{R}eproducibility vs. computational efficiency on {HPC}
                      systems},
      reportid     = {FZJ-2024-03087},
      year         = {2024},
      abstract     = {HPC systems have particular hard- and software
                      configurations that introduce specific challenges for the
                      implementation of reproducible data processing workflows.
                      The DataLad based 'FAIRly big workflow' allows for a
                      separation of the compute environment from the processing
                      pipeline enabling automatic reproducibility over systems.
                      Yet, the sheer size of RAM and CPUs on HPC systems will
                      allow for different ways to optimize compute jobs in
                      contrast to compute clusters and certainly the average
                      workstation/laptop. In this talk, I discuss general
                      differences between HCP and more standard compute
                      environments regarding necessary choices for the setup of
                      processing pipelines to be reproducible. Among the main
                      factors are the availability of RAM, local storage, inodes
                      and wall clock time.},
      month         = {Apr},
      date          = {2024-04-04},
      organization  = {Distribits: technologies for
                       distributed data management,
                       Düsseldorf (Germany), 4 Apr 2024 - 4
                       Apr 2024},
      subtyp        = {Other},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/1025704},
}