Modern supercomputing nodes are typically highly heterogeneous and exhibit Non-Uniform Memory Access (NUMA) characteristics. Automatically mapping tasks on such NUMA architectures while maintaining high operational efficiency poses significant challenges. In this paper, we propose a novel automatic mapping algorithm called NAAM (NUMA-Aware Auto Mapping), which accounts for both intra-node and inter-node heterogeneity. NAAM dynamically adjusts mapping strategies for various tasks across heterogeneous NUMA systems by performing a dynamic search to identify optimal mappings for typical heterogeneous hardware configurations. Finally, we conduct extensive experiments to validate the effectiveness of our method, with results showing that NAAM outperforms baseline approaches by up to 8% in terms of speedup over the default mapping.