Abstract: This paper addresses the application bottlenecks of domestic Phytium multi-core NUMA architecture server platforms in high-performance computing scenarios,conducting multi-level optimization research around the SP benchmark application.The paper proposes and implements optimization strategies at four levels:compilation,memory allocation,NUMA topology,and vectorized reduction.Experiments analyze the execution time,parallel efficiency,and speedup for different dataset sizes running on 1 MPI process and 8 MPI processes with varying numbers of parallel cores.The analysis shows that the optimized computation time is significantly reduced.With 8 MPI processes and 128 parallel cores,the performance of medium to large datasets improves by 3 to 5 times,and performance degradation under high concurrency is alleviated.The optimized parallel efficiency is more linear,achieving multiple-fold improvements for small datasets with 8 MPI processes,while medium to large datasets maintain approximately 90%,85%,and 64%~71% efficiency at 32,64,and 128 parallel cores respectively,delaying the onset of performance saturation.In terms of speedup,8 MPI processes show better performance gains than 1 MPI process under high concurrency.Experimental results demonstrate that the proposed multi-level optimization strategies can effectively enhance the computational performance of SP applications on the target server architecture.Especially as the number of cores increases and NUMA effects become more pronounced,the optimization scheme exhibits strong scalability advantages,providing an optimization path for numerical simulation and scientific computing on domestic high-performance computing platforms.

Key words: SP applications, Non-uniform memory access, MPI, OpenMP, NEON