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

Technical Report · Fri Dec 31 23:00:00 EST 2021

DOI:https://doi.org/10.2172/1843500· OSTI ID:1843500

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

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

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.

• Research Organization: US Department of Energy (USDOE), Washington DC (United States). Office of Science
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
• OSTI ID: 1843500
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING