FOGA: Flag Optimization with Genetic Algorithm
This work addresses compiler optimization for embedded systems with limited resources, but it is incremental as it builds on existing autotuning methods.
The paper tackles the problem of compiler flag optimization for embedded systems by introducing FOGA, a genetic algorithm-based autotuning solution, which demonstrated a remarkable speedup in execution time compared to the state-of-the-art framework OpenTuner.
Recently, program autotuning has become very popular especially in embedded systems, when we have limited resources such as computing power and memory where these systems run generally time-critical applications. Compiler optimization space gradually expands with the renewed compiler options and inclusion of new architectures. These advancements bring autotuning even more important position. In this paper, we introduced Flag Optimization with Genetic Algorithm (FOGA) as an autotuning solution for GCC flag optimization. FOGA has two main advantages over the other autotuning approaches: the first one is the hyperparameter tuning of the genetic algorithm (GA), the second one is the maximum iteration parameter to stop when no further improvement occurs. We demonstrated remarkable speedup in the execution time of C++ source codes with the help of optimization flags provided by FOGA when compared to the state of the art framework OpenTuner.