Tootfinder

13CFLUX - Third-generation high-performance engine for isotopically (non)stationary 13C metabolic flux analysis
Anton Stratmann, Martin Bey{\ss}, Johann F. Jadebeck, Wolfgang Wiechert, Katharina N\"oh
https://arxiv.org/abs/2509.23847

13CFLUX - Third-generation high-performance engine for isotopically (non)stationary 13C metabolic flux analysis
13C-based metabolic flux analysis (13C-MFA) is a cornerstone of quantitative systems biology, yet its increasing data complexity and methodological diversity place high demands on simulation software. We introduce 13CFLUX(v3), a third-generation simulation platform that combines a high-performance C++ engine with a convenient Python interface. The software delivers substantial performance gains across isotopically stationary and nonstationary analysis workflows, while remaining flexible to acco…

Chromo: A High-Performance Python Interface to Hadronic Event Generators for Collider and Cosmic-Ray Simulations
Anatoli Fedynitch, Hans Dembinski, Anton Prosekin
https://arxiv.org/abs/2507.21856

Chromo: A High-Performance Python Interface to Hadronic Event Generators for Collider and Cosmic-Ray Simulations
Simulations of hadronic and nuclear interactions are essential in both collider and astroparticle physics. The Chromo package provides a unified Python interface to multiple widely used hadronic event generators, including EPOS, DPMJet, Sibyll, QGSJet, and Pythia. Built on top of their original Fortran and C++ implementations, Chromo offers a zero-overhead abstraction layer suitable for use in Python scripts, Jupyter notebooks, or from the command line, while preserving the performance of direc…

Automated Test Data Generation for Enterprise Protobuf Systems: A Metaclass-Enhanced Statistical Approach
Y. Du
https://arxiv.org/abs/2507.22070 https://ar…

Automated Test Data Generation for Enterprise Protobuf Systems: A Metaclass-Enhanced Statistical Approach
Large-scale enterprise systems utilizing Protocol Buffers (protobuf) present significant challenges for performance testing, particularly when targeting intermediate business interfaces with complex nested data structures. Traditional test data generation approaches are inadequate for handling the intricate hierarchical and graph-like structures inherent in enterprise protobuf schemas. This paper presents a novel test data generation framework that leverages Python's metaclass system for dynami…

We have finally documented Ruff – the tool greatly simplifies static code analysis for Python projects: #Python

Ruff
Ruff is an extremely fast Python linter and code formatter written in Rust that can enforce the rules of flake8, isort, perflint, Black, Bandit, and others. In total, Ruff can check over 800 rules....

Mojo: MLIR-Based Performance-Portable HPC Science Kernels on GPUs for the Python Ecosystem
William F. Godoy, Tatiana Melnichenko, Pedro Valero-Lara, Wael Elwasif, Philip Fackler, Rafael Ferreira Da Silva, Keita Teranishi, Jeffrey S. Vetter
https://arxiv.org/abs/2509.21039

Mojo: MLIR-Based Performance-Portable HPC Science Kernels on GPUs for the Python Ecosystem
We explore the performance and portability of the novel Mojo language for scientific computing workloads on GPUs. As the first language based on the LLVM's Multi-Level Intermediate Representation (MLIR) compiler infrastructure, Mojo aims to close performance and productivity gaps by combining Python's interoperability and CUDA-like syntax for compile-time portable GPU programming. We target four scientific workloads: a seven-point stencil (memory-bound), BabelStream (memory-bound), miniBUDE (co…

I think my code would work perfectly except that os.utime does not actually work...
#python #code #programming

rStar2-Agent: Agentic Reasoning Technical Report
Ning Shang, Yifei Liu, Yi Zhu, Li Lyna Zhang, Weijiang Xu, Xinyu Guan, Buze Zhang, Bingcheng Dong, Xudong Zhou, Bowen Zhang, Ying Xin, Ziming Miao, Scarlett Li, Fan Yang, Mao Yang
https://arxiv.org/abs/2508.20722

rStar2-Agent: Agentic Reasoning Technical Report
We introduce rStar2-Agent, a 14B math reasoning model trained with agentic reinforcement learning to achieve frontier-level performance. Beyond current long CoT, the model demonstrates advanced cognitive behaviors, such as thinking carefully before using Python coding tools and reflecting on code execution feedback to autonomously explore, verify, and refine intermediate steps in complex problem-solving. This capability is enabled through three key innovations that makes agentic RL effective at…

from my link log —
Taichi: high-performance parallel programming in Python.
https://www.taichi-lang.org/
saved 2025-02-15 https://dot…

I made it onto the #Python Bytes podcast with a contribution on measuring the performance of prek: https://pythonbytes.fm/episodes/show/448/im-getting-the-bios-flavor

I'm Getting the BIOS Flavor
News and announcements from the Python community for the week of Sep 8th, 2025

Who Wins the Race? (R Vs Python) - An Exploratory Study on Energy Consumption of Machine Learning Algorithms
Rajrupa Chattaraj, Sridhar Chimalakonda, Vibhu Saujanya Sharma, Vikrant Kaulgud
https://arxiv.org/abs/2508.17344

Who Wins the Race? (R Vs Python) - An Exploratory Study on Energy Consumption of Machine Learning Algorithms
The utilization of Machine Learning (ML) in contemporary software systems is extensive and continually expanding. However, its usage is energy-intensive, contributing to increased carbon emissions and demanding significant resources. While numerous studies examine the performance and accuracy of ML, only a limited few focus on its environmental aspects, particularly energy consumption. In addition, despite emerging efforts to compare energy consumption across various programming languages for s…