Abstract: To improve the performance of genetic process mining algorithm for handling large scale event log, a GPU-based parallel genetic process mining algorithm is proposed. Since traditional binary chromosome coding method can not represent the AND-Split/AND-Join and the OR-Split/OR-Join structures in causal matrix, a new coding method of chromosome is proposed. The proposed method can effectively solve the problem of genetic representation of causal matrix on graphics processing units (GPU) by three arrays, which are content, labels and position. Meanwhile, the efficient genetic crossover/mutation operators and a parallel method of fitness value computation are designed and implemented. Simulation experiments show that the proposed algorithm, compared with the CPU-based genetic process mining algorithm, has obvious advantages in precision and convergence rate, and moreover it obtains speedup of 36.4 and 47.2 on two data sets respectively.