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037 _ _ |a FZJ-2024-03087
100 1 _ |a Hoffstaedter, Felix
|0 P:(DE-Juel1)131684
|b 0
|e Corresponding author
111 2 _ |a Distribits: technologies for distributed data management
|c Düsseldorf
|d 2024-04-04 - 2024-04-04
|w Germany
245 _ _ |a Reproducibility vs. computational efficiency on HPC systems
260 _ _ |c 2024
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a Other
|2 DataCite
336 7 _ |a INPROCEEDINGS
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336 7 _ |a conferenceObject
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336 7 _ |a LECTURE_SPEECH
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336 7 _ |a Conference Presentation
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|2 PUB:(DE-HGF)
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520 _ _ |a 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.
536 _ _ |a 5254 - Neuroscientific Data Analytics and AI (POF4-525)
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|c POF4-525
|f POF IV
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909 C O |o oai:juser.fz-juelich.de:1025704
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910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)131684
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-525
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Decoding Brain Organization and Dysfunction
|9 G:(DE-HGF)POF4-5254
|x 0
914 1 _ |y 2024
920 1 _ |0 I:(DE-Juel1)INM-7-20090406
|k INM-7
|l Gehirn & Verhalten
|x 0
980 _ _ |a conf
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)INM-7-20090406
980 _ _ |a UNRESTRICTED


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