The LCLStream Ecosystem for Multi-Institutional Dataset Exploration

Read full text
Authors: David O. Williams Rogers, Valerio Mariani, Cong Wang, Ryan Coffee, W. Kroeger, Murali Shankar, Hans Thorsten Schwander, Thomas Beck, Frédéric Poitevin, Jana Thayer
Cited by: 0
References: 23

Abstract

We describe a new end-to-end experimental data streaming framework designed from the ground up to support new types of applications – AI training, extremely high-rate X-ray time-of-flight analysis, crystal structure determination with distributed processing, and custom data science applications and visualizers yet to be created. Throughout, we use design choices merging cloud microservices with traditional HPC batch execution models for security and flexibility. This project makes a unique contribution to the DOE Integrated Research Infrastructure (IRI) landscape. By creating a flexible, API-driven data request service, we address a significant need for high-speed data streaming sources for the X-ray science data analysis community. With the combination of data request API, mutual authentication web security framework, job queue system, high-rate data buffer, and complementary nature to facility infrastructure, the LCLStreamer framework has prototyped and implemented several new paradigms critical for future generation experiments.

Fields of Study & Subjects

Scientific Computing and Data Management Distributed and Parallel Computing Systems Algorithms and Data Compression

Publication details

Pages
433–442
Publication date
22 January 2026
Language
EN