{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/accelerating-time-to-science-by-streaming","title":"Accelerating Time-to-Science by Streaming Detector Data Directly into Perlmutter Compute Nodes","arxiv_id":"2403.14352","date":"2024-03-21","proceeding":null,"authors":["Samuel S. Welborn","Bjoern Enders","Chris Harris","Peter Ercius","Deborah J. Bard"],"abstract":"Recent advancements in detector technology have significantly increased the size and complexity of experimental data, and high-performance computing (HPC) provides a path towards more efficient and timely data processing. However, movement of large data sets from acquisition systems to HPC centers introduces bottlenecks owing to storage I/O at both ends. This manuscript introduces a streaming workflow designed for an high data rate electron detector that streams data directly to compute node memory at the National Energy Research Scientific Computing Center (NERSC), thereby avoiding storage I/O. The new workflow deploys ZeroMQ-based services for data production, aggregation, and distribution for on-the-fly processing, all coordinated through a distributed key-value store. The system is integrated with the detector's science gateway and utilizes the NERSC Superfacility API to initiate streaming jobs through a web-based frontend. Our approach achieves up to a 14-fold increase in data throughput and enhances predictability and reliability compared to a I/O-heavy file-based transfer workflow. Our work highlights the transformative potential of streaming workflows to expedite data analysis for time-sensitive experiments.","url_abs":"https://arxiv.org/abs/2403.14352v3","url_pdf":"https://arxiv.org/pdf/2403.14352v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"accelerating-time-to-science-by-streaming","repo_url":"https://github.com/swelborn/streaming-paper-with-code","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}