Navigating the Multilingual Landscape of Scientific Computing: Python, Julia, and Awkward Array

Scientific computing relies heavily on powerful tools like Julia and Python. While Python has long been the preferred choice in High Energy Physics (HEP) data analysis, there’s a growing interest in migrating legacy software to Julia. We explore language interoperability, focusing on how Awkward Array data structures can connect Julia and Python. We discuss memory management, data buffer copies, and dependency handling, highlighting performance gains from invoking Julia from Python and vice versa. Particularly, we look into distributed array-oriented calculations involving large-scale HEP data and a unique role of Awkward Array in these workflows. We examine the advantages and challenges of achieving interoperability between Julia and Python in scientific computing.

Authors

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-10-07
DOI
https://doi.org/10.1051/epjconf/202533701004/pdf
Primary Topic
Scientific Computing and Data Management
Type
article
Field-Weighted Citation Impact
0.00
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article

Navigating the Multilingual Landscape of Scientific Computing: Python, Julia, and Awkward Array

Ianna Osborne, Jerry Ling
Springer Link (Chiba Institute of Technology)
Scientific Computing and Data Management
article

Navigating the Multilingual Landscape of Scientific Computing: Python, Julia, and Awkward Array

Ianna Osborne, Jerry Ling
article en

Abstract

Scientific computing relies heavily on powerful tools like Julia and Python. While Python has long been the preferred choice in High Energy Physics (HEP) data analysis, there’s a growing interest in migrating legacy software to Julia. We explore language interoperability, focusing on how Awkward Array data structures can connect Julia and Python. We discuss memory management, data buffer copies, and dependency handling, highlighting performance gains from invoking Julia from Python and vice versa. Particularly, we look into distributed array-oriented calculations involving large-scale HEP data and a unique role of Awkward Array in these workflows. We examine the advantages and challenges of achieving interoperability between Julia and Python in scientific computing.

Springer Link (Chiba Institute of Technology)
Openalex Percentile: Top 11%
Scientific Computing and Data Management
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