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Extending PyJL - Transpiling Python Libraries to Julia

Authors Miguel Marcelino , António Menezes Leitão

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OASIcs.SLATE.2022.6.pdf
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Author Details

Miguel Marcelino
  • INESC-ID/Instituto Superior Técnico University of Lisbon, Portugal
António Menezes Leitão
  • INESC-ID/Instituto Superior Técnico University of Lisbon, Portugal

Funding

This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) (references PTDC/ART-DAQ/31061/2017 and UIDB/50021/2020).

Cite AsGet BibTex

Miguel Marcelino and António Menezes Leitão. Extending PyJL - Transpiling Python Libraries to Julia. In 11th Symposium on Languages, Applications and Technologies (SLATE 2022). Open Access Series in Informatics (OASIcs), Volume 104, pp. 6:1-6:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/OASIcs.SLATE.2022.6

Abstract

Many high-level programming languages have emerged in recent years. Julia is one of these languages, claiming to offer the speed of C, the macro capabilities of Lisp, and the user-friendliness of Python. Julia’s syntax is one of its major strengths, making it ideal for scientific and numerical computing. Furthermore, Julia’s high-performance on modern hardware makes it an appealing alternative to Python. However, Python has a considerable advantage over Julia: its extensive library set. There have been efforts to make Python libraries available to Julia either through Foreign Function Interfaces (FFI’s), or through manual translation, but both have their tradeoffs: FFI’s do not take advantage of Julia’s performance, as they call Python’s Virtual Machine, and manual translation is demanding and time-consuming. To address these issues and bridge the gap between the two languages, we propose PyJL, a transpilation tool that converts Python source-code to human-readable Julia source-code. Although the development of PyJL is still at an early stage, our preliminary results reveal that the generated code follows the pragmatics of Julia and is capable of high performance.

Subject Classification

ACM Subject Classification
  • Software and its engineering → Source code generation
  • General and reference → Cross-computing tools and techniques
Keywords
  • Source-to-Source Compiler
  • Automatic Transpilation
  • Library Translation
  • Python
  • Julia

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References

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