Accelerating Half-Precision Seismic Simulation on Neural Processing Unit

Yinuo Wang

doi:10.1109/TPDS.2025.3584773

Accelerating Half-Precision Seismic Simulation on Neural Processing Unit

2025-10-01

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DOI：10.1109/TPDS.2025.3584773

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Due to the superiority of handling irregular regions of interest, the curvilinear grid finite difference method (CGFDM) has become wildely used in seismic simulation for earthquake hazard evaluation and understanding of earthquake physics. This paper proposes a novel approach that optimizes a CGFDM solver on the Ascend, a cutting-edge Neural Processing Unit (NPU) using half-precision storage and mixed-precision arithmetic. The approach increases the data throughput and computing efficiency, enabling more effective seismic modeling. Furthermore, we propose an efficient matrix unit enabled 3D difference algorithm that employs matrix unit on NPU to accelerate the computation. By fully exploiting the capability of matrix unit and wide SIMD lane, our solver on Ascend achieves a speedup of 4.19 × over the performance of parallel solver on two AMD CPUs and has successfully simulated real-world Wenchuan earthquake. To the best of our knowledge, we are the first to conduct seismic simulations on NPU.

Keywords:

Seismic simulation

half-precision

finite difference method

AI accelerator

ascend

NPU

Accelerating Half-Precision Seismic Simulation on Neural Processing Unit

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