Abstract. Recent compilers provided many of optimization passes, thus even for
expert programmers it is really hard to know which compiler optimization among
several optimizations passes can improve program performance. Moreover, the
search space for optimization sequences is very huge.Therefore, finding an optimal
sequence for even a simple code is not an easy task.The goal of this paper is to find a
set of a good optimization sequences using parallel genetic algorithm. Themethod
firstly classifies the programs into three clusters then applying three versions of
genetic algorithms each one to cluster in parallel. In order to enhance the result,
the migration strategy between these three algorithms is applied. Three optimal
sequences at the same time are obtained from this method. However, the proposed
method improved the execution time on average by 87% compared with the O2
optimization flag. This method also outperforms the sequential version of genetic
algorithm on average of the execution time by 74.8% in case of using Tournament
selection and 72.5% in case of multi-selection method. LLVM framework is used
to validate and execute the proposed method. In addition, Polybench, Standerford,
Shootout benchmarks are used as case study to verify the effectiveness of the
proposed method.