As the scale of cities continues to increase, urban transportation systems are facing more and more challenges, such as traffic congestion and traffic safety. Traffic simulation is a method to solve urban traffic problems. It uses virtual and real computing technologies to process real-time traffic data and optimize urban traffic efficiency. It is an important method to achieve the parallel city theory in intelligent transportation. However, traditional computing systems often encounter problems such as insufficient computing resources and long simulation delays when running large-scale urban traffic simulations. To solve the above problems, this study proposes a parallel algorithm for traffic simulation of parallel cities based on the parallel city theory and the heterogeneous architecture of China’s new-generation supercomputer, Tianhe. This algorithm accurately simulates traffic elements such as vehicles, roads, and traffic signals, and applies methods such as road network division, parallel driving of vehicles, and parallel control of signal lights to achieve high-performance traffic simulation. The algorithm runs on Tianhe, a supercomputing platform with 16 nodes and more than 25 000 cores, and simulates real traffic scenarios involving 2.4 million vehicles, 7 797 intersections, and 170 000 lanes within the Fifth Ring Road in Beijing. Compared with traditional single-node simulation, the proposed algorithm reduces the simulation time of each step from 2.21 s to 0.37 s, achieving nearly 6 times acceleration. An urban traffic simulation with a scale of one million vehicles has been successfully implemented on a domestic heterogeneous supercomputing platform.