COMET: X86 Cost Model Explanation Framework

Chaudhary, Isha, Renda, Alex, Mendis, Charith, Singh, Gagandeep

arXiv.org Artificial Intelligence 

ML-based program cost models have been shown to yield fairly accurate program cost predictions. They can replace heavily-engineered analytical program cost models in mainstream compilers, but their black-box nature discourages their adoption. In this work, we propose the first framework, COMET, for generating faithful, generalizable, and intuitive explanations for x86 cost models. COMET brings interpretability specifically to ML-based cost models, such as Ithemal. We generate and compare COMET's explanations for Ithemal against COMET's explanations for a hand-crafted, accurate analytical model, uiCA. Our empirical findings show an inverse correlation between the error in the cost prediction of a cost model and the prominence of semantically-richer features in COMET's explanations for the cost model for a given x86 basic block.

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