COMET: Neural Cost Model Explanation Framework
MLSys(2023)
摘要
Cost models predict the cost of executing given assembly code basic blocks on
a specific microarchitecture. Recently, neural cost models have been shown to
be fairly accurate and easy to construct. They can replace heavily engineered
analytical cost models used in mainstream compiler workflows. However, their
black-box nature discourages their adoption. In this work, we develop the first
framework, COMET, for generating faithful, generalizable, and intuitive
explanations for neural cost models. We generate and compare COMET's
explanations for the popular neural cost model, Ithemal against those for an
accurate CPU simulation-based cost model, uiCA. Our empirical findings show an
inverse correlation between the prediction errors of Ithemal and uiCA and the
granularity of basic block features in COMET's explanations for them, thus
indicating potential reasons for the higher error of Ithemal with respect to
uiCA.
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