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Numba: a LLVM-based Python JIT compiler

Authors:

Siu Kwan Lam,

Antoine Pitrou,

Stanley SeibertAuthors Info & Claims

LLVM '15: Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC

Article No.: 7, Pages 1 - 6

https://doi.org/10.1145/2833157.2833162

Published: 15 November 2015 Publication History

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Abstract

Dynamic, interpreted languages, like Python, are attractive for domain-experts and scientists experimenting with new ideas. However, the performance of the interpreter is often a barrier when scaling to larger data sets. This paper presents a just-in-time compiler for Python that focuses in scientific and array-oriented computing. Starting with the simple syntax of Python, Numba compiles a subset of the language into efficient machine code that is comparable in performance to a traditional compiled language. In addition, we share our experience in building a JIT compiler using LLVM[1].

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Index Terms

Numba: a LLVM-based Python JIT compiler
1. Software and its engineering
  1. Software notations and tools
    1. Compilers

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LLVM '15: Proceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC

November 2015

74 pages

ISBN:9781450340052

DOI:10.1145/2833157

Conference Chair:
Hal Finkel
Argonne National Laboratory

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: 15 November 2015

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Air Force Research Laboratory

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SC15: The International Conference for High Performance Computing, Networking, Storage and Analysis

November 15, 2015

Texas, Austin

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LLVM '15 Paper Acceptance Rate 7 of 12 submissions, 58%;

Overall Acceptance Rate 16 of 22 submissions, 73%

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Bringing low-level languages to the JVM: efficient execution of LLVM IR on Truffle

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Bringing low-level languages to the JVM: efficient execution of LLVM IR on Truffle

Debugging native extensions of dynamic languages

Poster: connecting PGAS and traditional HPC languages

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