Abstract
Finding the best optimization sequence order that can improve the performance for even a simple program is not an easy task. However, the modern compilers provide dozens of optimizations, making it not a practical solution to try all the optimization sequences manually to find the optimal one. In this paper, genetic algorithm is proposed to select the best optimization sequence for a cluster of similar programs. However, wide set of programs are elected to cover as much as possible all the features. The set of the programs are classified into three clusters depending on them features. Thus, the genetic algorithm in this work is learning method. This means any new program, unseen program, can take the optimization sequence of the cluster that has similar features to it. Moreover, two scenarios are proposed using genetic algorithm to find the best optimization sequence for each cluster. In the first scenario, programs are classified into three clusters according to program dynamic features. The genetic algorithm with Tournament selection method is applied on each cluster independently to obtain a good optimization sequence for a cluster. Moreover, the proposed method improved the execution time on average by 77% compared with the O2. The second scenario was exactly similar to the first one. While, different selection methods are used for each cluster. The improved average execution time for this scenario was 78% compared with the O2. LLVM framework is used to validate and execute the proposed method. In addition, Bolybench, Standerford, Shootout benchmarks are used to verify the effectiveness of the proposed method.
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Almohammed, M.H., Alwan, E.H., Fanfakh, A.B.M. (2020). Programs Features Clustering to Find Optimization Sequence Using Genetic Algorithm. In: Jain, L., Peng, SL., Alhadidi, B., Pal, S. (eds) Intelligent Computing Paradigm and Cutting-edge Technologies. ICICCT 2019. Learning and Analytics in Intelligent Systems, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-030-38501-9_4
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DOI: https://doi.org/10.1007/978-3-030-38501-9_4
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