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

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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Publication Details

Journal
EPJ Web of Conferences
Published
2025-01-01
DOI
https://doi.org/10.1051/epjconf/202533701283
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Douglas Thain, Barry Sly-Delgado, C. Moore, Benjamín Tovar et al.
EPJ Web of Conferences
Distributed and Parallel Computing Systems
article

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

Douglas Thain, Barry Sly-Delgado, C. Moore, Benjamín Tovar, Zhou Jin, A. Townsend, K. Lannon
article en

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.

EPJ Web of ConferencesVol. 337
University of Notre Dame (US)
Openalex Percentile: Top 67%
Distributed and Parallel Computing Systems
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