Ensuring the long-term reproducibility of data analyses requires results stability tests to verify that analysis results remain within acceptable variation bounds despite inevitable software updates and hardware evolutions. This paper introduces a numerical variability approach for results stability tests, which determines acceptable variation bounds using random rounding of floating-point calculations. By applying the resulting stability test tofMRIPrep, a widely-used neuroimaging tool, we show that the test is sensitive enough to detect subtle updates in image processing methods while remaining specific enough to accept numerical variations within a reference version of the application. This result contributes to enhancing the reliability and reproducibility of data analyses by providing a robust and flexible method for stability testing.

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.

Mondrian Forests are a powerful data stream classification method, but their large memory footprint makes them ill-suited for low-resource platforms such as connected objects. We explored using reduced-precision floating-point representations to lower memory consumption and evaluated its effect on classification performance. We applied the Mondrian Forest implementation provided by OrpailleCC, a C++ collection of data stream algorithms, to two canonical datasets in human activity recognition: Recofit and Banos et al. Results show that the precision of floating-point values used by tree nodes can be reduced from 64 bits to 8 bits with no significant difference in F1 score. In some cases, reduced precision was shown to improve classification performance, presumably due to its regularization effect. We conclude that numerical precision is a relevant hyperparameter in the Mondrian Forest, and that commonly-used double precision values may not be necessary for optimal performance. Future work will evaluate the generalizability of these findings to other data stream classifiers.

VeriTracer automatically instruments a code and traces the accuracy of floating-point variables over time. VeriTracer enriches the visual traces with contextual information such as the call site path in which a value was modified. Contextual information is important to understand how the floating-point errors propagate in complex codes. VeriTracer is implemented as an LLVM compiler tool on top of Verificarlo. We demonstrate how VeriTracer can detect accuracy loss and quantify the impact of using a compensated algorithm on ABINIT, an industrial HPC application for Ab Initio quantum computation.

Summary Current architecture complexity requires fine tuning of compiler and runtime parameters to achieve best performance. Autotuning substantially improves default parameters in many scenarios, but it is a costly process requiring long iterative evaluations. We propose an automatic piecewise autotuner based on CERE (Codelet Extractor and REplayer). CERE decomposes applications into small pieces called codelets: Each codelet maps to a loop or to an OpenMP parallel region and can be replayed as a standalone program. Codelet autotuning achieves better speedups at a lower tuning cost. By grouping codelet invocations with the same performance behavior, CERE reduces the number of loops or OpenMP regions to be evaluated. Moreover, unlike whole‐program tuning, CERE customizes the set of best parameters for each specific OpenMP region or loop. We demonstrate the CERE tuning of compiler optimizations, number of threads, thread affinity, and scheduling policy on both nonuniform memory access and heterogeneous architectures. Over the NAS benchmarks, we achieve an average speedup of 1.08× after tuning. Tuning a codelet is 13× cheaper than whole‐program evaluation and predicts the tuning impact with a 94.7% accuracy. Similarly, exploring thread configurations and scheduling policies for a Black‐Scholes solver on an heterogeneous big.LITTLE architecture is over 40× faster using CERE.