# RISC-Based 10K+ Core Finite Difference Method Accelerator for CFD
## 1 Introduction
It enables acceleration by inserting directives into the code to identify regions suitable for parallelization, utilizing OpenACC directives and runtime library routines. This approach avoids the need for significant algorithmic modifications to adapt to specific GPU architectures and compilers. Nevertheless, the current OpenACC specification and its supporting compilers are not yet fully defined and optimized, remaining under active development and refinement [28]. This underscores persistent challenges in achieving high-performance CFD on general-purpose computing hardware, including algorithmic compatibility, parallel efficiency, and memory bandwidth limitations. As emphasized in NASA's CFD Vision 2030 Study: A Path to Revolutionary Computational Aerodynamics, the evolution of CFD is inextricably linked to innovations in computational platforms [29]. Consequently, future research must prioritize the development of specialized CFD accelerators with novel parallel architectures, guided by numerical models derived from practical applications and tightly integrated with algorithmic characteristics. The fundamental reliance on approximations within CFD computations highlights the potential of Approximate Computing paradigms to manage the inherent accuracy-cost trade-offs more effectively. Recent research demonstrates promising approaches for hardware-level approximation. [PERSON] et al. [30] introduced Approximate and Exact Multi-Processor System-on-Chip (AxE) platform, a heterogeneous RISC-V-based MPSoC (Multi-Processor System-on-Chip) platform integrating both exact and approximate cores,achieving significant speedups (32%) and energy savings (21%) while maintaining high accuracy (99.3%) for suitable workloads. Similarly, [PERSON] et al. [31] proposed a quality-scalable LMS filter using algorithmic-level approximations introduced by an external quality knob, enabling runtime power savings (5-32%) adaptable to required precision levels. These works exemplify the growing importance of exploiting hardware-aware approximations for computationally intensive domains, providing valuable context for designing specialized accelerators. Professors [PERSON] and [PERSON] have categorized the research domains of CFD into six branches [32], represented by the acronym M5A, comprising five \"M\" components and one \"A\". The five \"M\" terms denote Methods, Meshing, Machines, Mapping, and Mechanisms, while \"A\" signifies Applications. Among these, Method constitutes the cornerstone of CFD research and the most active branch, encompassing diverse numerical approaches such as the finite difference method (FDM), finite volume method (FVM), and finite element method (FEM) [33]. Machine includes computers and supercomputers, which are the hardware resources of CFD. Focusing on the \"Methods\" and \"Machines\" aspects, this study leverages the classical FDM as its foundation and proposes a novel reduced instruction set multi-core microprocessor architecture specifically designed to accelerate FDM-based CFD computations. As a classical discretization approach in CFD numerical analysis and simulation, the FDM serves as the foundation of this study. Leveraging the \"Methods\" paradigm of CFD, this work focuses on computational acceleration for FDM-based solvers and proposes a reduced instruction set multi-core microprocessor architecture dedicated to accelerating CFD computations. The designed accelerator demonstrates network programmability and achieves significant acceleration performance. The main contributions of this work are as follows. * Application-Specific Microprocessor Architecture (FCore) and Instruction Set Design. The characteristics of the finite difference method were studied. Based on these characteristics, the FCore microprocessor architecture and its corresponding instruction set architecture (F-RISC) were designed. * Dedicated Network Structure (FMesh). The FMesh network structure was developed.