A Systematic Approach to Model-Guided Empirical Search for Memory

Abstract. The goal of this work is a systematic approach to compiler optimization for simultaneously optimizing across multiple levels of the memory hierarchy. Our approach combines compiler models and heuris-tics with guided empirical search to take advantage of their complemen-tary strengths. The models and heuristics limit the search to a small number of candidate implementations, and the empirical results provide accurate feedback information to the compiler. In previous work, we pro-pose a compiler algorithm for deriving a set of parameterized solutions, followed by a model-guided empirical search to determine the best in-teger parameter values and select the best overall solution. This paper focuses on formalizing the process of deriving parameter values, which is a multi-variable optimization problem, and considers the role of AI search techniques in deriving a systematic framework for the search.