Linking scientific instruments and computation: Patterns, technologies, and experiences

Rafael Vescovi; Ryan Chard; Nickolaus D Saint; Ben Blaiszik; Jim Pruyne; Tekin Bicer; Alex Lavens; Zhengchun Liu; Michael E Papka; Suresh Narayanan; Nicholas Schwarz; Kyle Chard; Ian T Foster

doi:10.1016/j.patter.2022.100606

Linking scientific instruments and computation: Patterns, technologies, and experiences

Patterns (N Y). 2022 Oct 14;3(10):100606. doi: 10.1016/j.patter.2022.100606.

Authors

Rafael Vescovi¹, Ryan Chard¹, Nickolaus D Saint², Ben Blaiszik^{1

2}, Jim Pruyne^{1

2}, Tekin Bicer^{1

3}, Alex Lavens⁴, Zhengchun Liu¹, Michael E Papka^{5

6}, Suresh Narayanan³, Nicholas Schwarz³, Kyle Chard^{1

7}, Ian T Foster^{1

7}

Affiliations

¹ Data Science and Learning Division, Argonne National Laboratory, 9700 S. Cass Ave., Lemont, IL 60439, USA.
² Globus, University of Chicago, 5730 S. Ellis Ave., Chicago, IL 60615, USA.
³ X-ray Science Division, Argonne National Laboratory, 9700 S. Cass Ave., Lemont, IL 60439, USA.
⁴ Structural Biology Center, Argonne National Laboratory, 9700 S. Cass Ave., Lemont, IL 60439, USA.
⁵ Argonne Leadership Computing Facility, Argonne National Laboratory, 9700 S. Cass Ave., Lemont, IL 60439, USA.
⁶ Department of Computer Science, University of Illinois Chicago, 1200 W. Harrison St., Chicago, IL 60607, USA.
⁷ Department of Computer Science, University of Chicago, 5730 S. Ellis Ave., Chicago, IL 60615, USA.

Abstract

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. 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 review common patterns associated with such flows and describe methods 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.

Keywords: Experiment automation; Globus; big data; computing fabric; data fabric; machine learning; scientific facility; synchrotron light source; trust fabric; workflow.