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Python Interpreter Performance Deconstructed

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Gergö BaranyAuthors Info & Claims

Dyla'14: Proceedings of the Workshop on Dynamic Languages and Applications

Pages 1 - 9

https://doi.org/10.1145/2617548.2617552

Published: 09 June 2014 Publication History

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Abstract

The Python programming language is known for performing poorly on many tasks. While to some extent this is to be expected from a dynamic language, it is not clear how much each dynamic feature contributes to the costs of interpreting Python. In this study we attempt to quantify the costs of language features such as dynamic typing, reference counting for memory management, boxing of numbers, and late binding of function calls.

We use an experimental compilation framework for Python that can make use of type annotations provided by the user to specialize the program as well as elide unnecessary reference counting operations and function lookups. The compiled programs run within the Python interpreter and use its internal API to implement language semantics. By separately enabling and disabling compiler optimizations, we can thus measure how much each language feature contributes to total execution time in the interpreter.

We find that a boxed representation of numbers as heap objects is the single most costly language feature on numeric codes, accounting for up to 43% of total execution time in our benchmark set. On symbolic object-oriented code, late binding of function and method calls costs up to 30%. Redundant reference counting, dynamic type checks, and Python's elaborate function calling convention have comparatively smaller costs.

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Reinicke AEngbrecht TSchüttensack LEchterhof T(2024)Application of an Artificial Neural Network for Efficient Computation of Chemical Activities within an EAF Process ModelMetals10.3390/met1406073614:6(736)Online publication date: 20-Jun-2024
https://doi.org/10.3390/met14060736
Bernstein MBolz-Tereick CMonat RRubio-González C(2024)Dr Wenowdis: Specializing Dynamic Language C Extensions using Type InformationProceedings of the 13th ACM SIGPLAN International Workshop on the State Of the Art in Program Analysis10.1145/3652588.3663316(1-8)Online publication date: 20-Jun-2024
https://dl.acm.org/doi/10.1145/3652588.3663316
Zhang ZXing ZXia XXu XZhu LLu Q(2023)Faster or Slower? Performance Mystery of Python Idioms Unveiled with Empirical Evidence2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)10.1109/ICSE48619.2023.00130(1495-1507)Online publication date: May-2023
https://doi.org/10.1109/ICSE48619.2023.00130
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Index Terms

Python Interpreter Performance Deconstructed
1. Software and its engineering
  1. Software notations and tools
    1. Compilers
      1. Interpreters

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Dyla'14: Proceedings of the Workshop on Dynamic Languages and Applications

June 2014

45 pages

ISBN:9781450329163

DOI:10.1145/2617548

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 09 June 2014

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PLDI '14: ACM SIGPLAN Conference on Programming Language Design and Implementation

June 9 - 11, 2014

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Reinicke AEngbrecht TSchüttensack LEchterhof T(2024)Application of an Artificial Neural Network for Efficient Computation of Chemical Activities within an EAF Process ModelMetals10.3390/met1406073614:6(736)Online publication date: 20-Jun-2024
https://doi.org/10.3390/met14060736
Bernstein MBolz-Tereick CMonat RRubio-González C(2024)Dr Wenowdis: Specializing Dynamic Language C Extensions using Type InformationProceedings of the 13th ACM SIGPLAN International Workshop on the State Of the Art in Program Analysis10.1145/3652588.3663316(1-8)Online publication date: 20-Jun-2024
https://dl.acm.org/doi/10.1145/3652588.3663316
Zhang ZXing ZXia XXu XZhu LLu Q(2023)Faster or Slower? Performance Mystery of Python Idioms Unveiled with Empirical Evidence2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)10.1109/ICSE48619.2023.00130(1495-1507)Online publication date: May-2023
https://doi.org/10.1109/ICSE48619.2023.00130
Katsaragakis MPapadopoulos LKonijnenburg MCatthoor FSoudris D(2023)A memory footprint optimization framework for Python applications targeting edge devicesJournal of Systems Architecture10.1016/j.sysarc.2023.102936142(102936)Online publication date: Sep-2023
https://doi.org/10.1016/j.sysarc.2023.102936
Zhang QXu LXu B(2023)Python meets JIT compilers: A simple implementation and a comparative evaluationSoftware: Practice and Experience10.1002/spe.326754:2(225-256)Online publication date: 5-Sep-2023
https://doi.org/10.1002/spe.3267
Zhang QXu LXu B(2022)RegCPython: A Register-based Python Interpreter for Better PerformanceACM Transactions on Architecture and Code Optimization10.1145/356897320:1(1-25)Online publication date: 16-Dec-2022
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Titzer B(2022)A fast in-place interpreter for WebAssemblyProceedings of the ACM on Programming Languages10.1145/35633116:OOPSLA2(646-672)Online publication date: 31-Oct-2022
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Zhang QXu LZhang XXu B(2022)Quantifying the interpretation overhead of PythonScience of Computer Programming10.1016/j.scico.2021.102759215:COnline publication date: 1-Mar-2022
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Martins do Rosario VMikio Hato MAzevedo RBorin E(2018)A Methodology for Optimization of Interpreters2018 Symposium on High Performance Computing Systems (WSCAD)10.1109/WSCAD.2018.00040(205-212)Online publication date: Oct-2018
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RegCPython: A Register-based Python Interpreter for Better Performance

Compiler to interpreter: experiences with a distributed programming language

Development of internal domain-specific languages: design principles and design patterns

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