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February 5, 20260 citations

Reshaping Analysis for Fast Turnaround: Leveraging Concurrency to Reduce Latency in Late-Stage LHC Analysis Workflows

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KLKevin LannonCMConnor MooreBSBarry Sly-DelgadoUniversity of Notre Dame

Key Points

  • The aim is to reduce turnaround time in late-stage LHC data analysis workflows by leveraging concurrency.
  • Reshaped analysis applications on a large-scale system using thousands of nodes.
  • Implemented changes across storage systems, data management, and application design.
  • Optimized task scheduling to enhance performance.
  • Achieved significant reductions in analysis turnaround time.
  • Demonstrated improved efficiency in data processing and task execution.

Abstract

In the data analysis pipeline for LHC experiments, a key aspect is the step in which particle-level data is reduce to summary statistics allowing insights to be extracted through statistical analysis. Here, we will refer to this step as “analysis.” Analysis is a very important part of the pipeline as it is the step where individual researchers exercise their creativity in trying new ideas in the pursuit of discovery. Therefore, a critical metric for the analysis step is turnaround time because it determines how rapidly researchers can explore their space of ideas. We demonstrate our experience reshaping latestage analysis applications on thousands of nodes with the goal of minimizing turnaround time. It is not enough merely to increase scale: it is necessary to make changes throughout the stack, including storage systems, data management, task scheduling, and application design.

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Cite This Study

Lannon et al. (2025) studied this question.

synapsesocial.com/papers/698434f9f1d9ada3c1fb3cfbhttps://doi.org/10.1051/epjconf/202533701283/pdf
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