BMOA: Baseline-Mechanism-Outcome Attribution for Compiler-Induced Numerical Deviations

2026-07-29

AI Summary

This work addresses the challenge of accurately attributing floating-point numerical discrepancies introduced by compilers. To this end, it proposes BMOA, a diagnostic framework that systematically disentangles three dimensions of such deviations: baseline comparison relationships, compiler-induced mechanistic evidence, and precision consequences. Integrating rigorous floating-point semantics, local transformation analysis, cross-compiler comparisons, high-precision validation, and reproducibility testing, BMOA generates auditable and traceable attribution records while preserving ambiguous attributions when evidence is insufficient. Evaluation on six scientific computing kernels targeting the ARM64 platform yielded 1,276 attribution records and 162 mechanistic instances, revealing that the choice of baseline significantly influences diagnostic conclusions and demonstrating that compiler-introduced deviations do not necessarily entail actual precision loss.

Abstract

Formalizing compiler-aware numerical correctness requires distinguishing what an observed floating-point difference means, what compiler behavior the evidence supports, and what numerical consequence follows. Existing testing workflows often collapse these questions into a pass/fail mismatch. We introduce Baseline--Mechanism--Outcome Attribution (BMOA), a diagnostic framework that separates the comparison relation and system boundary, the evidence-supported compiler mechanism, and the reference-qualified accuracy outcome. BMOA combines operational strict floating-point, transformation-local, reproducibility, cross-compiler, and higher-precision comparisons, while preserving mixed, ambiguous, and unknown attributions when evidence is insufficient. Each record retains inputs, configurations, numerical metrics, and supporting artifacts for audit. We evaluate BMOA on six scientific-computing kernels, deterministic stress-input families, and controlled Clang configurations on ARM64. A 1,276-record attribution corpus and a 162-instance controlled mechanism matrix show that baseline choice changes diagnoses, compiler-induced deviation does not imply accuracy loss, and cancellation and large dynamic range expose the strongest effects within the targeted matrix. BMOA converts raw mismatches into explicit, auditable, evidence-bounded records. Although it is not itself a proof system, these records provide an empirical foundation for future formal specifications and proof obligations for compiler-aware numerical correctness.

Problem

• compiler-induced numerical deviations
• floating-point correctness
• numerical accuracy
• attribution framework
• scientific computing

Innovation

• BMOA
• compiler-induced numerical deviation
• floating-point correctness
• attribution framework
• numerical reproducibility