Dataflow Optimized Reconfigurable Acceleration for FEM-based CFD Simulations

2024-11-25

arXiv.org

AI Summary

To address the computational intensity, memory bandwidth bottlenecks, and high power consumption inherent in finite element method (FEM) solvers for the Navier–Stokes equations in computational fluid dynamics (CFD), this paper proposes a dataflow-optimized reconfigurable accelerator architecture tailored for FEM-CFD workloads. Implemented on an AMD Alveo U200 FPGA using high-level synthesis (HLS), the design integrates customized dataflow scheduling, sparse matrix compression storage, and multi-level on-chip cache co-optimization to jointly optimize computation, memory access, and pipeline depth—while preserving numerical accuracy. Experimental results demonstrate a 7.9× speedup over an optimized Vitis-HLS baseline implementation. Against a high-end server CPU executing software-based FEM-CFD, the accelerator reduces latency by 45% and power consumption by 62.3%, achieving a 3.64× improvement in energy efficiency. This advancement significantly enhances both real-time capability and energy-efficiency of FEM-CFD simulations.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: (Computational) Cognitive Architectures
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Accelerating FEM-based CFD simulations efficiently
• Reducing compute and memory intensity in Navier-Stokes solutions
• Optimizing FPGA performance for high-precision fluid flow analysis

Innovation

• FPGA accelerator for Navier-Stokes equations
• High-Level Synthesis (HLS) implementation
• Reconfigurable FEM-based CFD optimization