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Linking scientific instruments and computation: Patterns, technologies, and experiences
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DOI：10.1016/j.patter.2022.100606.png)
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摘要
En 中文
Powerful detectors at modern experimental facilities routinely collect data atmultiple GB/s. Online analysis methods are needed to enable the collection of only interesting subsets of such massive data streams, such as by explicitly discarding some data elements or by directing instruments to relevant areas of experimental space. Thus, methods are required for configuring and running distributed computing pipelines-what we call flows-that link instruments, computers (e.g., for analysis, simulation, artificial intelligence [AI] model training), edge computing (e.g., for analysis), data stores, metadata catalogs, and high-speed networks. We reviewcommon patterns associated with such flows and describemethods for instantiating these patterns. We present experiences with the application of these methods to the processing of data from five different scientific instruments, each of which engages powerful computers for data inversion,model training, or other purposes. We also discuss implications of such methods for operators and users of scientific facilities.
Keyword:
PHOTON-CORRELATION SPECTROSCOPY
DIGITAL TWINS
SCIENCE
WORKFLOW
RECONSTRUCTION
INTEGRATION
SERVICE
SYSTEM
TOOL
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期刊
IF:
7.4
论文数:
953
被引数:
3.6K
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