Tootfinder

Had an issue with #NixOS where I couldn't run a #Python tool that uses pandas. (Nix really doesn't work well with Python.)
Somebody helped me to find the solution by:
nix-shell -p 'python3.withPackages (ps: with ps; [ pandas numpy ])'
Today, another Python project with pandas a…

Finch: Sparse and Structured Array Programming with Control Flow
Willow Ahrens, Teodoro Fields Collin, Radha Patel, Kyle Deeds, Changwan Hong, Saman Amarasinghe
https://arxiv.org/abs/2404.16730

Finch: Sparse and Structured Array Programming with Control Flow
From FORTRAN to NumPy, arrays have revolutionized how we express computation. However, arrays in these, and almost all prominent systems, can only handle dense rectilinear integer grids. Real world arrays often contain underlying structure, such as sparsity, runs of repeated values, or symmetry. Support for structured data is fragmented and incomplete. Existing frameworks limit the array structures and program control flow they support to better simplify the problem. In this work, we propose …

This https://arxiv.org/abs/2401.00865 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDC_…

Xorbits: Automating Operator Tiling for Distributed Data Science
Data science pipelines commonly utilize dataframe and array operations for tasks such as data preprocessing, analysis, and machine learning. The most popular tools for these tasks are pandas and NumPy. However, these tools are limited to executing on a single node, making them unsuitable for processing large-scale data. Several systems have attempted to distribute data science applications to clusters while maintaining interfaces similar to single-node libraries, enabling data scientists to sca…

This https://arxiv.org/abs/2312.14949 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

LLM Interactive Optimization of Open Source Python Libraries -- Case Studies and Generalization
With the advent of large language models (LLMs) like GPT-3, a natural question is the extent to which these models can be utilized for source code optimization. This paper presents methodologically stringent case studies applied to well-known open source python libraries pillow and numpy. We find that contemporary LLM ChatGPT-4 (state September and October 2023) is surprisingly adept at optimizing energy and compute efficiency. However, this is only the case in interactive use, with a human exp…

Leveraging Quadratic Polynomials in Python for Advanced Data Analysis
Olena Voloshkina, Anastasiia Kovalova, Rostyslav Sipakov
https://arxiv.org/abs/2402.06133

Leveraging Quadratic Polynomials in Python for Advanced Data Analysis
The article provides a comprehensive overview of using quadratic polynomials in Python for modeling and analyzing data. It starts by explaining the basic concept of a quadratic polynomial, its general form, and its significance in capturing the curvature in data indicative of natural phenomena. The paper highlights key features of quadratic polynomials, their applications in regression analysis, and the process of fitting these polynomials to data using Python's `numpy` and `matplotlib` librari…

This https://arxiv.org/abs/2401.17345 has been replaced.
link: https://scholar.google.com/scholar?q=a

Reproducibility, energy efficiency and performance of pseudorandom number generators in machine learning: a comparative study of python, numpy, tensorflow, and pytorch implementations
Pseudo-Random Number Generators (PRNGs) have become ubiquitous in machine learning technologies because they are interesting for numerous methods. The field of machine learning holds the potential for substantial advancements across various domains, as exemplified by recent breakthroughs in Large Language Models (LLMs). However, despite the growing interest, persistent concerns include issues related to reproducibility and energy consumption. Reproducibility is crucial for robust scientific inq…