Pythran: Crossing the Python Frontier

Guelton, Serge

doi:10.1109/mcse.2018.021651342

Cited by 9 publications

(12 citation statements)

References 3 publications

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“…The Python API is built automatically when fluidfft is installed. 5 It first generates the Cython source code as a pair of .pyx and .pxd files containing a class wrapping its C++ counterpart. 6 The Cython files are produced from template files (specialized for the 2D and 3D cases) using the template library mako.…”

Section: Code Organizationmentioning

confidence: 99%

“…However, it is more convenient and recommended to use an operator class, as described in the example for Python API. Although the operator classes can function as pure Python code, some of its critical methods can be compiled, if Pythran [5] is available during installation of fluidfft. We will show towards the end of this section that by using Pythran, we reach the performance of the equivalent Fortran code.…”

Section: Code Organizationmentioning

confidence: 99%

“…As already mentioned, we use Pythran [5] to compile some critical "operator" functions. In this subsection, we present a microbenchmark for one simple task used in pseudo-spectral codes: projecting a velocity field on a nondivergent velocity field.…”

Section: Microbenchmark Of Critical "Operator" Functionsmentioning

confidence: 99%

“…https://fluidfft.readthedocs.io/en/latest/tutorials. html 5. Detailed steps for installation are provided in the documentation 6.…”

mentioning

confidence: 99%

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FluidFFT: Common API (C++ and Python) for Fast Fourier Transform HPC Libraries

Mohanan¹,

Bonamy²,

Augier

2019

JORS

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The Python package fluidfft provides a common Python API for performing Fast Fourier Transforms (FFT) in sequential, in parallel and on GPU with different FFT libraries (FFTW, P3DFFT, PFFT, cuFFT). fluidfft is a comprehensive FFT framework which allows Python users to easily and efficiently perform FFT and the associated tasks, such as computing linear operators and energy spectra. We describe the architecture of the package composed of C++ and Cython FFT classes, Python "operator" classes and Pythran functions. The package supplies utilities to easily test itself and benchmark the different FFT solutions for a particular case and on a particular machine. We present a performance scaling analysis on three different computing clusters and a microbenchmark showing that fluidfft is an interesting solution to write efficient Python applications using FFT.

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Section: Code Organizationmentioning

confidence: 99%

Section: Code Organizationmentioning

confidence: 99%

Section: Microbenchmark Of Critical "Operator" Functionsmentioning

confidence: 99%

“…https://fluidfft.readthedocs.io/en/latest/tutorials. html 5. Detailed steps for installation are provided in the documentation 6.…”

mentioning

confidence: 99%

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FluidFFT: Common API (C++ and Python) for Fast Fourier Transform HPC Libraries

Mohanan¹,

Bonamy²,

Augier

2019

JORS

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“…2 The goal is to optimize such that "80 percent of the runtime is spent in 20 percent of the code" [10]. Here, Cython [3] and Pythran [8] compilers comes in handy. An example on how we use Pythran to reach similar performance than with Fortran by writing only Python code is described in the companion paper on fluidfft [11].…”

Section: Balance Between Runtime Efficiency and Cost Of Developmentmentioning

confidence: 99%

FluidSim: Modular, Object-Oriented Python Package for High-Performance CFD Simulations

Mohanan¹,

Bonamy²,

Linares³

et al. 2019

JORS

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The Python package fluidsim is introduced in this article as an extensible framework for Computational Fluid Mechanics (CFD) solvers. It is developed as a part of FluidDyn project [2], an effort to promote opensource and open-science collaboration within fluid mechanics community and intended for both educational as well as research purposes. Solvers in fluidsim are scalable, High-Performance Computing (HPC) codes which are powered under the hood by the rich, scientific Python ecosystem and the Application Programming Interfaces (API) provided by fluiddyn and fluidfft packages [11]. The present article describes the design aspects of fluidsim, which includes use of Python as the main language; focus on the ease of use, reuse and maintenance of the code without compromising performance. The implementation details including optimization methods, modular organization of features and object-oriented approach of using classes to implement solvers are also briefly explained. Currently, fluidsim includes solvers for a variety of physical problems using different numerical methods (including finite-difference methods). However, this metapaper shall dwell only on the implementation and performance of its pseudo-spectral solvers, in particular the two-and three-dimensional Navier-Stokes solvers. We investigate the performance and scalability of fluidsim in a state of the art HPC cluster. Three similar pseudo-spectral CFD codes based on Python (Dedalus, SpectralDNS) and Fortran (NS3D) are presented and qualitatively and quantitatively compared to fluidsim. The source code is hosted at Bitbucket as a Mercurial repository bitbucket.org/fluiddyn/fluidsim and the documentation generated using Sphinx can be read online at fluidsim.readthedocs.io.

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Python meets JIT compilers: A simple implementation and a comparative evaluation

Zhang,

Xu,

2023

Softw Pract Exp

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Developing a just‐in‐time (JIT) compiler can be a daunting task, especially for a language as flexible as Python. While PyPy, powered with JIT compilation, can often outperform the official pure interpreter, CPython, by a noteworthy margin, its popularity remains far from comparable to that of CPython due to some issues. Given that an easier‐to‐deploy and better‐compatible JIT compiler would benefit more Python users, we have developed comPyler, a simple JIT compiler functioning as a CPython extension and intended to convert frequently interpreted CPython bytecode into equivalent machine code. Designed with good compatibility in mind, it does not alter CPython's internal data structures or external interfaces. Based on LLVM's mature infrastructure, it can be readily ported to almost all platforms. Compared with CPython, it achieved the highest speedup of 2.205, with an average of 1.093. Despite its relatively limited effect, comPyler incurs low development costs. As a baseline compiler, it also sheds light on the improvement attainable by optimizing solely the overhead of bytecode interpretation. Furthermore, as there is still a dearth of empirical research covering the multitude of JIT compilers available for Python, we have conducted a performance study that examines Jython, IronPython, PyPy, GraalPy, Pyston, Pyjion, and our comPyler. Our research takes into account not only the benchmark speed for various time windows but also the boot latency and memory footprint. Through this comprehensive study, our objective is to assist developers in gaining a better understanding of the effects of distinct JIT compilation techniques and to aid users in making informed decisions when choosing among different Python implementations.

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Pythran: Crossing the Python Frontier

Cited by 9 publications

References 3 publications

FluidFFT: Common API (C++ and Python) for Fast Fourier Transform HPC Libraries

FluidFFT: Common API (C++ and Python) for Fast Fourier Transform HPC Libraries

FluidSim: Modular, Object-Oriented Python Package for High-Performance CFD Simulations

Python meets JIT compilers: A simple implementation and a comparative evaluation

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