Function merging is a pivotal technique for reducing code size by combining identical or similar functions into a single function. While prior research has extensively explored this technique, it has not been assessed in conjunction with function outlining and linker’s identical code folding, despite substantial common ground. The traditional approaches necessitate the complete intermediate representation to compare functions. Consequently, none of these approaches offer a scalable solution compatible with separate compilations while achieving global function merging, which is critical for large app development. In this paper, we introduce our global function merger, leveraging global merge information from previous code generation runs to optimistically create merging instances within each module context independently. Notably, our approach remains sound even when intermediate representations change, making it well-suited for distributed build environments. We present a comprehensive code generation framework that can seamlessly operate both the state-of-the-art global function outliner and our global function merger. These components work in harmony with each other. Our assessment shows that this approach can lead to a 3.5% reduction in code size and a 9% decrease in build time.

Function layout, also known as function reordering or function placement, is one of the most effective profile-guided compiler optimizations. By reordering functions in a binary, compilers can improve the performance of large-scale applications or reduce the compressed size of mobile applications. Although the technique has been extensively studied in the context of large-scale binaries, no study has thoroughly investigated function layout algorithms on mobile applications. In this article, we develop the first principled solution for optimizing function layouts in the mobile space. To this end, we identify two key optimization goals: reducing the compressed code size and improving the cold start-up time of a mobile application. Then, we propose a formal model for the layout problem, whose objective closely matches our goals, and a novel algorithm for optimizing the layout. The method is inspired by the classic balanced graph partitioning problem. We have carefully engineered and implemented the algorithm in an open-source compiler, Low-level Virtual Machine (LLVM). An extensive evaluation of the new method on large commercial mobile applications demonstrates improvements in start-up time and compressed size compared to the state-of-the-art approach. 1

Function layout, also known as function reordering or function placement, is one of the most effective profile-guided compiler optimizations. By reordering functions in a binary, compilers can improve the performance of large-scale applications or reduce the compressed size of mobile applications. Although the technique has been extensively studied in the context of large-scale binaries, no study has thoroughly investigated function layout algorithms on mobile applications.

Positive user experience of mobile apps demands they not only launch fast and run fluidly, but are also small in order to reduce network bandwidth from regular updates. Conventional optimizations often trade off size regressions for performance wins, making them impractical in the mobile space. Indeed, profile-guided optimization (PGO) is successful in server workloads, but is not effective at reducing size and page faults for mobile apps. Also, profiles must be collected from instrumenting builds that are up to 2X larger, so they cannot run normally on real mobile devices.

Many real-life problems require optimizing functions with expensive evaluations. Bayesian Optimization (BO) and Search-based Optimization (SO) are two broad families of algorithms that try to find the global optima of a function with the goal of minimizing the number of function evaluations. A large body of existing work deals with the single-fidelity setting, where function evaluations are very expensive but accurate. However, in many applications, we have access to multiple-fidelity functions that vary in their cost and accuracy of evaluation. In this paper, we propose a novel approach called Multi-fidelity Hybrid (MF-Hybrid) that combines the best attributes of both BO and SO methods to discover the global optima of a black-box function with minimal cost. Our experiments on multiple benchmark functions show that the MF-Hybrid algorithm outperforms existing single-fidelity and multi-fidelity optimization algorithms.