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Due to the large number of optimizations provided in modern compilers and to compiler optimization specific opportunities, a Design Space Exploration (DSE) is necessary to search for the best sequence of compiler optimizations for a given code fragment (e.g., function). As this exploration is a complex and time consuming task, we present new DSE strategies to reduce the exploration time and still select optimization sequences able to improve the performance of each function. The DSE is based on a clustering approach which groups functions with similarities and then explore the reduced search space provided by the optimizations previously suggested for the functions in each group. The identification of similarities between functions uses a data mining method which is applied to a symbolic representation of the source code. The DSE strategies uses the reduced optimizations set identified by clustering in two ways: as the design space or as the initial configuration of the algorithm. In both ways, the adoption of a pre-selection based on clustering allows the use of simple and fast DSE algorithms. Several experiments for evaluating the effectiveness of the proposed approach address the exploration of compiler optimization sequences. Besides, we investigate the impact of each technique or component employed in the selection process. Experimental results reveal that the use of our new clustering-based DSE approach achieved a significant reduction on the total exploration time of the search space at the same time that obtained performance speedups close to a traditional genetic algorithm-

Exploração de sequências de otimização do compilador baseada em técnicas hibridas de mineração de dados complexos