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.

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Publication Details

Journal
EPJ Web of Conferences
Published
2025-01-01
DOI
https://doi.org/10.1051/epjconf/202533701004
Primary Topic
Computational Physics and Python Applications
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

J. Pivarski, Jerry Ling, I. Osborne
EPJ Web of Conferences
Computational Physics and Python Applications
article

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

J. Pivarski, Jerry Ling, I. Osborne
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.

EPJ Web of ConferencesVol. 337
Princeton University (US), Harvard University Press (US)
National Science Foundation
Openalex Percentile: Top 85%
Computational Physics and Python Applications
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