Enabling discovery data science through cross-facility workflows Article Swipe
Katie Antypas
,
Deborah Bard
,
Johannes Blaschke
,
R. S. Canon
,
Bjoern Enders
,
Mallikarjun Arjun Shankar
,
Suhas Somnath
,
Dale Stansberry
,
Thomas Uram
,
Sean Wilkinson
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/bigdata52589.2021.9671421
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/bigdata52589.2021.9671421
Experimental and observational instruments for scientific research (such as light sources, genome sequencers, accelerators, telescopes and electron microscopes) increasingly require High Performance Computing (HPC) scale capabilities for data analysis and workflow processing. Next-generation instruments are being deployed with higher resolutions and faster data capture rates, creating a big data crunch that cannot be handled by modest institutional computing resources. Often these big data analysis pipelines also require near real-time computing and have higher resilience requirements than the simulation and modeling workloads more traditionally seen at HPC centers. While some facilities have enabled workflows to run at a single HPC facility, there is a growing need to integrate capabilities across HPC facilities to enable cross-facility workflows, either to provide resilience to an experiment, increase analysis throughput capabilities, or to better match a workflow to a particular architecture. In this paper we describe the barriers to executing complex data analysis workflows across HPC facilities and propose an architectural design pattern for enabling scientific discovery using cross-facility workflows that includes orchestration services, application programming interfaces (APIs), data access and co-scheduling.
Related Topics
Concepts
Workflow
Computer science
Orchestration
Supercomputer
Big data
Scheduling (production processes)
Petabyte
SPARK (programming language)
Distributed computing
Cloud computing
Data science
Computer architecture
Database
Operating system
Engineering
Musical
Art
Operations management
Visual arts
Programming language
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/bigdata52589.2021.9671421
- OA Status
- green
- Cited By
- 22
- References
- 27
- Related Works
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- OpenAlex ID
- https://openalex.org/W4206264294
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4206264294Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/bigdata52589.2021.9671421Digital Object Identifier
- Title
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Enabling discovery data science through cross-facility workflowsWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-12-15Full publication date if available
- Authors
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Katie Antypas, Deborah Bard, Johannes Blaschke, R. S. Canon, Bjoern Enders, Mallikarjun Arjun Shankar, Suhas Somnath, Dale Stansberry, Thomas Uram, Sean WilkinsonList of authors in order
- Landing page
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https://doi.org/10.1109/bigdata52589.2021.9671421Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://www.osti.gov/biblio/1847515Direct OA link when available
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Workflow, Computer science, Orchestration, Supercomputer, Big data, Scheduling (production processes), Petabyte, SPARK (programming language), Distributed computing, Cloud computing, Data science, Computer architecture, Database, Operating system, Engineering, Musical, Art, Operations management, Visual arts, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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22Total citation count in OpenAlex
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2025: 6, 2024: 10, 2023: 4, 2022: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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