Abstract
Performance of data reduction workflows at the High Flux Isotope Reactor (HFIR) and the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) is mainly determined by the time spent loading raw measurement events stored in large and sparse datasets. This paper describes: (1) our long-term view to leverage SNS and HFIR data management needs with our experience at ORNL’s world-class high performance computing (HPC) facilities, and (2) our short-term efforts to speed up current workflows using Mantid, a data analysis and reduction community framework used across several neutron scattering facilities. We show that minimally invasive short-term improvements in metadata management have a moderate impact in speeding up current production workflows. We propose a more disruptive domain-specific solution: the No Cost Input Output (NCIO) framework, we provide an overview, the risks and challenges in NCIO’s adoption by HFIR and SNS stakeholders.
W. F. Godoy et al.—Contributed Equally.
This manuscript has been co-authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
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Change history
18 December 2020
In the originally published version of the chapter 12, the reference 21 contained a mistake in the name of the author. The author’s name in the reference was changed to S. Hahn.
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Acknowledgements
A portion of this research used resources at the SNS, a Department of Energy (DOE) Office of Science User Facility operated by ORNL. ORNL is managed by UT-Battelle LLC for DOE under Contract DE-AC05-00OR22725.
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Godoy, W.F., Peterson, P.F., Hahn, S.E., Hetrick, J., Doucet, M., Billings, J.J. (2020). Performance Improvements on SNS and HFIR Instrument Data Reduction Workflows Using Mantid. In: Nichols, J., Verastegui, B., Maccabe, A.‘., Hernandez, O., Parete-Koon, S., Ahearn, T. (eds) Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI. SMC 2020. Communications in Computer and Information Science, vol 1315. Springer, Cham. https://doi.org/10.1007/978-3-030-63393-6_12
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