At present, parallelization technology is a dynamically evolving area in software development. Using parallelization to adapt software to existing multicore and multiprocessor systems, one can considerably increase the computational performance and significantly reduce the processing speed for big data arrays. High speed of software operation is important both for real-time data preprocessing and data analysis at all subsequent stages, including data visualization. This is the key requirement for the Large Hadron Collider (LHC) experiments. In this article, the results of using parallelization technology for analyzing LHCb physics data, with the jobs implemented in OSTAP based on pathos and pyROOT, are given.
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