Scalable Declarative HEP Analysis Workflows for Containerised Compute Clouds

• Main Authors: Tibor Šimko, Lukas Alexander Heinrich, Clemens Lange, Adelina Eleonora Lintuluoto, Danika Marina MacDonell, Audrius Mečionis, Diego Rodríguez Rodríguez, Parth Shandilya, Marco Vidal García
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2021-05-01
• Series: Frontiers in Big Data
• Subjects: computational workflows; reproducibility; scalability; declarative programming; analysis preservation
• Online Access: https://www.frontiersin.org/articles/10.3389/fdata.2021.661501/full

We describe a novel approach for experimental High-Energy Physics (HEP) data analyses that is centred around the declarative rather than imperative paradigm when describing analysis computational tasks. The analysis process can be structured in the form of a Directed Acyclic Graph (DAG), where each graph vertex represents a unit of computation with its inputs and outputs, and the graph edges describe the interconnection of various computational steps. We have developed REANA, a platform for reproducible data analyses, that supports several such DAG workflow specifications. The REANA platform parses the analysis workflow and dispatches its computational steps to various supported computing backends (Kubernetes, HTCondor, Slurm). The focus on declarative rather than imperative programming enables researchers to concentrate on the problem domain at hand without having to think about implementation details such as scalable job orchestration. The declarative programming approach is further exemplified by a multi-level job cascading paradigm that was implemented in the Yadage workflow specification language. We present two recent LHC particle physics analyses, ATLAS searches for dark matter and CMS jet energy correction pipelines, where the declarative approach was successfully applied. We argue that the declarative approach to data analyses, combined with recent advancements in container technology, facilitates the portability of computational data analyses to various compute backends, enhancing the reproducibility and the knowledge preservation behind particle physics data analyses.