COMET: Neural Cost Model Explanation Framework

Abstract

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 compilers. 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. We obtain an empirical inverse correlation between the prediction errors of Ithemal and uiCA and the granularity of basic block features in COMET’s explanations for them, indicating potential reasons for Ithemal’s higher error with respect to uiCA.