This research focuses on accelerating the finite-volume Computational Fluid Dynamics (CFD) solver, SENSEI, through concurrent CPU–GPU heterogeneous computing, leveraging multiple CPUs and GPUs. An overview of SENSEI, its discretization, and the heterogeneous computing workflow utilizing MPI and OpenACC are provided. A performance model for CPU–GPU heterogeneous computing, incorporating ghost cell exchange usually applied in stencil computations, is introduced to estimate the performance. This work explores the scaling performance of CPU–GPU heterogeneous computing in comparison to a pure multi-CPU/GPU setup using a supersonic inlet test case. The results emphasize the advantages of leveraging both CPU and GPU computational power concurrently. Highlighting the collaborative synergy of CPUs and GPUs as co-workers, this work shows improved performance over exclusive use of pure CPUs or GPUs, with the proposed performance model ensuring a fair estimation of these advantages. This work concludes by offering suggestions for application users interested in accelerating scientific computing simulations and providing feedback for hardware architects designing improved CPU–GPU heterogeneous systems for heterogeneous computing.

中文翻译：

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有限体积计算流体动力学求解器的 CPU-GPU 异构代码加速


这项研究的重点是通过并发 CPU-GPU 异构计算，利用多个 CPU 和 GPU 来加速有限体积计算流体动力学 (CFD) 求解器 SENSEI。概述了 SENSEI、其离散化以及利用 MPI 和 OpenACC 的异构计算工作流程。引入了 CPU-GPU 异构计算的性能模型来估计性能，该模型结合了通常应用于模板计算的幽灵单元交换。这项工作使用超音速入口测试用例，探讨了 CPU-GPU 异构计算与纯多 CPU/GPU 设置的扩展性能。结果强调了同时利用 CPU 和 GPU 计算能力的优势。这项工作强调了 CPU 和 GPU 作为同事的协作协同作用，显示出比单独使用纯 CPU 或 GPU 更高的性能，并且所提出的性能模型确保了对这些优势的公平评估。这项工作最后为有兴趣加速科学计算模拟的应用程序用户提供建议，并为为异构计算设计改进的 CPU-GPU 异构系统的硬件架构师提供反馈。