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Parallelization schemes are essential in order to exploit the full benefits of multi-core architectures. In said architectures, the most comprehensive parallelization API is OpenMP. However, the introduction of correct and optimal OpenMP parallelization to applications is not always a simple task, due to common parallel management pitfalls, architecture heterogeneity and the current necessity for human expertise in order to comprehend many fine details and abstract correlations. To ease this process, many automatic parallelization compilers were created over the last decade. Harel et al. [2020] tested several source-to-source compilers and concluded that each has its advantages and disadvantages and no compiler is superior to all other compilers in all tests. This indicates that a fusion of the compilers' best outputs under the best hyper-parameters for the current hardware setups can yield greater speedups. To create such a fusion, one should execute a computationally intensive hyper-parameter sweep, in which the performance of each option is estimated and the best option is chosen. We created a novel parallelization source-to-source multi-compiler named ComPar, which uses code segmentation-and-fusion with hyper-parameters tuning to achieve the best parallel code possible without any human intervention while maintaining the program's validity. In this paper we present ComPar and analyze its results on NAS and PolyBench benchmarks. We conclude that although the resources ComPar requires to produce parallel code are greater than other source-to-source parallelization compilers - as it depends on the number of parameters the user wishes to consider, and their combinations - ComPar achieves superior performance overall compared to the serial code version and other tested parallelization compilers. ComPar is publicly available at: https://github.com/Scientific-Computing-Lab-NRCN/compar.