PyTracer: Automatically profiling numerical instabilities in Python

TL;DR

PyTracer is a scalable Python profiler that automatically quantifies numerical instability in scientific computing applications, aiding developers in ensuring computational reliability.

Contribution

PyTracer introduces a scalable, automatic, and generic framework for profiling numerical stability in Python, filling a gap in existing analysis tools.

Findings

PyTracer effectively visualizes numerical instability in Python code.

It can evaluate stability using various noise models.

Demonstrated on SciPy and Scikit-learn functions.

Abstract

Numerical stability is a crucial requirement of reliable scientific computing. However, despite the pervasiveness of Python in data science, analyzing large Python programs remains challenging due to the lack of scalable numerical analysis tools available for this language. To fill this gap, we developed PyTracer, a profiler to quantify numerical instability in Python applications. PyTracer transparently instruments Python code to produce numerical traces and visualize them interactively in a Plotly dashboard. We designed PyTracer to be agnostic to numerical noise model, allowing for tool evaluation through Monte-Carlo Arithmetic, random rounding, random data perturbation, or structured noise for a particular application. We illustrate PyTracer's capabilities by testing the numerical stability of key functions in both SciPy and Scikit-learn, two dominant Python libraries for mathematical modeling. Through these evaluations, we demonstrate PyTracer as a scalable, automatic, and generic framework for numerical profiling in Python.