In the last decade, systems based on deep learning have significantly advanced for generating structured data from unstructured sources including images and sounds. However, determining what system has learned and on what basis it has reasoned is difficult. In contrast, inductive logic programming (ILP) offers a perfectly explainable approach for extracting relationships from structured data. However, it has efficiency limitations. To address this problem, several strategies have been proposed to accelerate the efficiency through parallelizing individual ILP system components. In this paper, we compare two popular parallelization attempts for the ILP system in the different levels, highlight their respective advantages, and explore their static and dynamic integration. Eventually, we demonstrate that some datasets can be processed remarkably efficiently using multi-grain parallel processing in ILP systems through several experiments.