Data-Aware Tridiagonal Solvers for CFD Applications
This work presents an optimized approach for solving large batches of tridiagonal systems that arise from the numerical discretization of 3D partial differential equations (PDEs) using compact finite difference schemes. These systems, common in computational fluid dynamics and other scientific computing applications, often suffer from performance bottlenecks—particularly in the x-direction—due to memory layout inefficiencies on both CPU and GPU architectures. To address this, we introduce a novel, direction-agnostic data structure that improves memory access patterns, enables vectorization, and enhances cache utilization.
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