Efficient Locality-aware Instruction Stream Scheduling for Stencil Computation on ARM Processors
Shanghao Liu, Hailong Yang, Xin You, Zhongzhi Luan, Yi Liu, Depei Qian · 2025
Stencil computation is one of the fundamental computational patterns in scientific computing, commonly adopted in solving partial differential equations (PDEs) and a wide range of application fields.However, due to the memory-bound nature, it is challenging to achieve satisfactory performance on the ARM many-core processors with complex computation and memory hierarchies.In this study, we propose independent instruction stream scheduling with the Serial-FMA to Tree-Based Reduction (SFTBR) technique to decompose the stencil computation into multiple independent instruction streams for improved instruction-level parallelism.Furthermore, we propose a locality-aware block scheduling technique for locality-aware multi-level thread parallelism to address the complexities of cache and memory hierarchies on modern ARM many-core processors.Based on the above techniques, we implement a domain-specific compiler, AOStencil, to automatically generate optimized stencil codes on ARM many-core processors with genetic-algorithm-driven parameter tuning.Our evaluation results demonstrate that AOStencil achieves up to 4.39× speedup over the state-ofthe-art domain-specific compilers on Kunpeng and Phytium platforms.