Optimizing FDTD Solvers for Electromagnetics: A Compiler-Guided Approach with High-Level Tensor Abstractions

• URL: http://arxiv.org/abs/2504.09118v1
• Date: Sat, 12 Apr 2025 08:08:12 GMT
• Title: Optimizing FDTD Solvers for Electromagnetics: A Compiler-Guided Approach with High-Level Tensor Abstractions
• Authors: Yifei He, Måns I. Andersson, Stefano Markidis,
• Abstract summary: We introduce an end-to-end domain-specific compiler based on the MLIR/LLVM infrastructure for Finite Difference Time Domain simulations.<n>We implement the three-dimensional kernel as operations on a 3D tensor abstraction with explicit computational semantics.<n>High-level optimizations such as loop tiling, fusion, and vectorization are automatically applied by the compiler.
• Score: 0.7373617024876725
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The Finite Difference Time Domain (FDTD) method is a widely used numerical technique for solving Maxwell's equations, particularly in computational electromagnetics and photonics. It enables accurate modeling of wave propagation in complex media and structures but comes with significant computational challenges. Traditional FDTD implementations rely on handwritten, platform-specific code that optimizes certain kernels while underperforming in others. The lack of portability increases development overhead and creates performance bottlenecks, limiting scalability across modern hardware architectures. To address these challenges, we introduce an end-to-end domain-specific compiler based on the MLIR/LLVM infrastructure for FDTD simulations. Our approach generates efficient and portable code optimized for diverse hardware platforms.We implement the three-dimensional FDTD kernel as operations on a 3D tensor abstraction with explicit computational semantics. High-level optimizations such as loop tiling, fusion, and vectorization are automatically applied by the compiler. We evaluate our customized code generation pipeline on Intel, AMD, and ARM platforms, achieving up to $10\times$ speedup over baseline Python implementation using NumPy.

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