In massively parallel processing (MPP) systems, an increase in data exchanges between processes often leads to higher time costs for these operations. Consequently, when the number of processes exceeds a certain threshold, the performance of a parallel program may degrade. This study examines the parallelization of an algorithm for finding the shortest path in a directed graph with cycles. The algorithm’s characteristics indicate that command-level parallelization yields limited efficiency. Meanwhile, the time complexity of the algorithm grows exponentially with increases in the number of vertices, edges, and cycles in the graph. Data-level parallelization is shown to be a viable approach. This paper presents a method to calculate the optimal number of processes for data-parallel algorithm optimization, based on the input data volume, to achieve maximum performance.