A methodology and framework for software memoization of functions

Enhancing performance is crucial when developing applications for high-performance and embedded computing. It requires sophisticated techniques and in-depth knowledge of the application domain and target architecture. Typically, developers prioritize the application's functional requirements over extra-functional requirements. Thus, a large part of the optimization effort is shifted to performance engineers, who rely on manual effort, alongside many analysis and optimization tools that need integration. This paper focuses on memoization, which caches results of pure computations and retrieves them if a function is called with repeating arguments. We propose a methodology for allowing developers and performance engineers to apply memoization straightforwardly by automating code analysis, code transformations, and memoization-specific profiling. It helps developers with no optimization expertise to quickly set up memoization and, simultaneously, it provides performance engineers with highly customizable analysis and memoization. We provide a concrete implementation supported by a DSL, a source-to-source compiler, and a memoization framework. We evaluate the methodology and framework with publicly available benchmarks. We show how one can analyze applications to select functions with performance improvement potential, which the experiments reveal might be challenging to find, and improve some applications with minimal effort.