Landscape of High-Performance Python to Develop Data Science and Machine Learning Applications

• Published in: ACM computing surveys Vol. 56; no. 3; pp. 1 - 30
• Main Authors: Castro, Oscar, Bruneau, Pierrick, Sottet, Jean-Sébastien, Torregrossa, Dario
• Format: Journal Article
• Language: English
• Published: New York, NY ACM 31.03.2024; Association for Computing Machinery
• Subjects: Algorithms; Computer science; Computing methodologies; Data science; Documents; Graphics processing units; Machine learning; Microprocessors; Parallel programming languages; Python; Software and its engineering; Software development techniques; data science; code acceleration; Python
• ISSN: 0360-0300; 1557-7341; 1557-7341
• DOI: 10.1145/3617588

Python has become the prime language for application development in the data science and machine learning domains. However, data scientists are not necessarily experienced programmers. Although Python lets them quickly implement their algorithms, when moving at scale, computation efficiency becomes inevitable. Thus, harnessing high-performance devices such as multi-core processors and graphical processing units to their potential is generally not trivial. The present narrative survey can be thought of as a reference document for such practitioners to help them make their way in the wealth of tools and techniques available for the Python language. Our document revolves around user scenarios, which are meant to cover most situations they may face. We believe that this document may also be of practical use to tool developers, who may use our work to identify potential lacks in existing tools and help them motivate their contributions.