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Modern high-performance processor systems universally employ hardware prefetch engines to address the "memory wall" issue. Nonetheless, prefetchers are typically activated with the default configuration at system startup, and this fixed configuration does not always achieve the intended performance in the face of varied programs and may even degrade performance. As a result, it is crucial to investigate the prefetch configuration tuning method that adapts to different program characteristics in order to take full advantage of hardware prefetching. In this study, a hardware prefetching tuning method based on program phase behavior is proposed to determine the prefetch configuration that maximizes the overall predicted performance of the program through low-overhead online profiling. In the profiling process, the branch instruction vector sampled by the hardware performance counter is used to dynamically classify the program phase behavior, and the performance profiling is performed for each type of phase. Simultaneously, the recurring program phases are no longer profiled to reduce overhead. Following the profiling, the prefetch configuration with the best predicted performance is derived by combining the performance data from each phase and its running time proportion. The results of the tests on prefetch-sensitive programs in SPEC2006, NPB, and PARSEC demonstrate that the prefetch configuration obtained using the suggested method has a geometric average performance improvement of 7.34% over the default configuration and achieves 99.34% of the optimal configuration. Furthermore, the profiling run adds only 2.24% extra overhead as compared to the default configuration. [ABSTRACT FROM AUTHOR]