Abstract: In the parallelization of Space-Time Adaptive Processing (STAP) arithmetic, traditional methods schedule the STAP arithmetic to different processors in the specific hardware architecture through coral-granularity division and improve the throughput by pipeline processing between processors. In the paper, its disadvantages are discussed from two perspectives: Coarse-grained scheduling hinders the parallelism; They are only suitable for the specific system parameters and hardware architectures. Thus, a new method based on fine-grained scheduling is put forward, which consists of three steps: Firstly, fine-grained task model in the form of Direct Acyclic Graph (DAG) is constructed; Secondly, the topology model is built to describe the target system; Finally, the established task model in fine-grained manner is assigned to different processors described in model topology. The experiment of the proposed method shows that it achieves better acceleration ratio, and more flexiable adaptation to different STAP applications.