  Powerful detectors at modern experimental facilities routinely collect data
at multiple 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. Such online analyses require methods for configuring and
running high-performance distributed computing pipelines--what we call
flows--linking instruments, HPC (e.g., for analysis, simulation, AI model
training), edge computing (for analysis), data stores, metadata catalogs, and
high-speed networks. In this article, we review common patterns associated with
such flows and describe methods for instantiating those patterns. We also
present experiences with the application of these methods to the processing of
data from five different scientific instruments, each of which engages HPC
resources for data inversion, machine learning model training, or other
purposes. We also discuss implications of these new methods for operators and
users of scientific facilities.
