Dataflow Optimized Reconfigurable Acceleration for FEM-based CFD Simulations

Anastassis Kapetanakis; Aggelos Ferikoglou; George Anagnostopoulos; Sotirios Xydis

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arXiv · 2411.16245

Dataflow Optimized Reconfigurable Acceleration for FEM-based CFD Simulations

Anastassis Kapetanakis·Aggelos Ferikoglou·George Anagnostopoulos·Sotirios Xydis

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Abstract

Computational Fluid Dynamics (CFD) simulations are essential for analyzing and optimizing fluid flows in a wide range of real-world applications. These simulations involve approximating the solutions of the Navier-Stokes differential equations using numerical methods, which are highly compute- and memory-intensive due to their need for high-precision iterations. In this work, we introduce a high-performance FPGA accelerator specifically designed for numerically solving the Navier-Stokes equations. We focus on the Finite Element Method (FEM) due to its ability to accurately model complex geometries and intricate setups typical of real-world applications. Our accelerator is implemented using High-Level Synthesis (HLS) on an AMD Alveo U200 FPGA, leveraging the reconfigurability of FPGAs to offer a flexible and adaptable solution. The proposed solution achieves 7.9x higher performance than optimized Vitis-HLS implementations and 45% lower latency with 3.64x less power compared to a software implementation on a high-end server CPU. This highlights the potential of our approach to solve Navier-Stokes equations more effectively, paving the way for tackling even more challenging CFD simulations in the future.

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Anastassis Kapetanakis, Aggelos Ferikoglou, George Anagnostopoulos, Sotirios Xydis. 2024-11-25. Dataflow Optimized Reconfigurable Acceleration for FEM-based CFD Simulations. https://arxiv.org/abs/2411.16245

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