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Efficient Data Management in Neutron Scattering Data Reduction Workflows at ORNL...

by William F Godoy, Peter F Peterson, Steven E Hahn, Jay J Billings
Publication Type
Conference Paper
Journal Name
IEEE International Conference on Big Data
Book Title
2020 IEEE International Conference on Big Data (Big Data)
Publication Date
Page Numbers
2674 to 2680
Publisher Location
District of Columbia, United States of America
Conference Name
International Workshop on Big Data Reduction held with 2020 IEEE International Conference on Big Data
Conference Location
Atlanta (virtual), Georgia, United States of America
Conference Sponsor
Conference Date

Oak Ridge National Laboratory (ORNL) experimental neutron science facilities produce 1.2 TB a day of raw event-based data that is stored using the standard metadata-rich NeXus schema built on top of the HDF5 file format. Performance of several data reduction workflows is largely determined by the amount of time spent on the loading and processing algorithms in Mantid, an open-source data analysis framework used across several neutron sciences facilities around the world. The present work introduces new data management algorithms to address identified input output (I/O) bottlenecks on Mantid. First, we introduce an in-memory binary-tree metadata index that resemble NeXus data access patterns to provide a scalable search and extraction mechanism. Second, data encapsulation in Mantid algorithms is optimally redesigned to reduce the total compute and memory runtime footprint associated with metadata I/O reconstruction tasks. Results from this work show speed ups in wall-clock time on ORNL data reduction workflows, ranging from 11% to 30% depending on the complexity of the targeted instrument-specific data. Nevertheless, we highlight the need for more research to address reduction challenges as experimental data volumes increase.