Position Papers for the ASCR Workshop on the Management and Storage of Scientific Data

Byna, Suren; Idreos, Stratos; Jones, Terry; Mohror, Kathryn; Ross, Rob; Rusu, Florin

doi:10.2172/1843500

Title: Position Papers for the ASCR Workshop on the Management and Storage of Scientific Data

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Abstract

The purpose of this workshop is to identify priority research directions in the area of data management for high-performance and scientific computing above and beyond HPC’s traditional "the parallel file system is the data-management system" model. Supporting the breadth of the DOE mission, including the explosion of AI uses and the growing needs of experimental and observational science, motivates revisiting our assumptions about data management. There are many facets of this topic to explore including: (1) Interfaces for accessing data that resides on traditional persistent storage as well as memory devices; (2) Storage-system architecture design that supports scientific workflows on varied hierarchical storage and networking devices; (3) Devising metadata management infrastructure to support FAIR principles (Findability, Accessibility, Interoperability, and Reusability); (4) Capturing provenance information about scientific data; (5) Utilizing AI to learn I/O patterns of emerging workloads for efficient data management; (6) Providing data management support for AI and complex workflows; and (7) Understanding the overlap between traditional storage systems and I/O (SSIO) efforts and data management. While the program committee has identified these topics as important areas for discussion, we welcome position papers from the community that propose additional topics of interest for discussion at the workshop. The workshopmore »« less

Authors:

Byna, Suren ^[1]; Idreos, Stratos ^[2]; Jones, Terry ^[3]; Mohror, Kathryn ^[1]; Ross, Rob ^[4]; Rusu, Florin ^[5]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Harvard Univ., Cambridge, MA (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Argonne National Lab. (ANL), Argonne, IL (United States)
Univ. of California, Merced, CA (United States)

Publication Date:: 2022-01-01

Research Org.:: US Department of Energy (USDOE), Washington DC (United States). Office of Science

Sponsoring Org.:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

OSTI Identifier:: 1843500

Resource Type:: Technical Report

Resource Relation:: Conference: ASCR Workshop on the Management and Storage of Scientific Data, Held Virtually (United States), 24-25, 27 Jan 2022; Related Information: https://www.orau.gov/MgmtStgeonScData

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING

Citation Formats


                    Byna, Suren, Idreos, Stratos, Jones, Terry, Mohror, Kathryn, Ross, Rob, and Rusu, Florin. Position Papers for the ASCR Workshop on the Management and Storage of Scientific Data.  United States: N. p., 2022. 
        Web.  doi:10.2172/1843500.

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                    Byna, Suren, Idreos, Stratos, Jones, Terry, Mohror, Kathryn, Ross, Rob, & Rusu, Florin. Position Papers for the ASCR Workshop on the Management and Storage of Scientific Data.  United States.  https://doi.org/10.2172/1843500

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                    Byna, Suren, Idreos, Stratos, Jones, Terry, Mohror, Kathryn, Ross, Rob, and Rusu, Florin. 2022.  
        "Position Papers for the ASCR Workshop on the Management and Storage of Scientific Data".  United States.  https://doi.org/10.2172/1843500.  https://www.osti.gov/servlets/purl/1843500.

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@article{osti_1843500,

  title        = {Position Papers for the ASCR Workshop on the Management and Storage of Scientific Data},

  author       = {Byna, Suren and Idreos, Stratos and Jones, Terry and Mohror, Kathryn and Ross, Rob and Rusu, Florin},

  abstractNote = {The purpose of this workshop is to identify priority research directions in the area of data management for high-performance and scientific computing above and beyond HPC’s traditional "the parallel file system is the data-management system" model. Supporting the breadth of the DOE mission, including the explosion of AI uses and the growing needs of experimental and observational science, motivates revisiting our assumptions about data management. There are many facets of this topic to explore including: (1) Interfaces for accessing data that resides on traditional persistent storage as well as memory devices; (2) Storage-system architecture design that supports scientific workflows on varied hierarchical storage and networking devices; (3) Devising metadata management infrastructure to support FAIR principles (Findability, Accessibility, Interoperability, and Reusability); (4) Capturing provenance information about scientific data; (5) Utilizing AI to learn I/O patterns of emerging workloads for efficient data management; (6) Providing data management support for AI and complex workflows; and (7) Understanding the overlap between traditional storage systems and I/O (SSIO) efforts and data management. While the program committee has identified these topics as important areas for discussion, we welcome position papers from the community that propose additional topics of interest for discussion at the workshop. The workshop agenda will include breakout sessions for discussing these and selected topic areas to inform priority research directions for data management for high-performance and scientific computing.},

  doi          = {10.2172/1843500},

  url          = {https://www.osti.gov/biblio/1843500},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2022},

  month        = {1}

}

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