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PyTracer: Automatically Profiling Numerical Instabilities in Python
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DOI：10.1109/TC.2022.3224377.png)
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Abstract
En 中文
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. PyTracertransparently instruments Python code to produce numerical traces and visualize them interactively in a Plotly dashboard. We designed PyTracerto be agnostic to numerical noise model, allowing for numerical profiling 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 PyTraceras a scalable, automated, and generic framework for numerical profiling in Python.
Keywords:
Instruments
Python
Arithmetic
Numerical stability
Numerical models
Libraries
Codes
Computer arithmetic
quality analysis and evaluation
stochastic analysis
tracing
visualization techniques and methodologies
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3.8
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5.4K
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