Accelerating CFD simulation with high order finite difference method on curvilinear coordinates for modern GPU clusters
Chuangchao Ye, Pengjunyi Zhang, Rui Yan, Dejun Sun, Zhenhua Wan

TL;DR
This paper presents a hardware-aware GPU acceleration approach for high order finite difference CFD simulations on curvilinear grids, achieving up to 2000x speedup over CPU implementations.
Contribution
It introduces a set of hardware-aware optimizations for high order finite difference CFD solvers on GPUs, enabling efficient simulations on modern heterogeneous HPC systems.
Findings
Achieved up to 2000x speedup over CPU-based solvers.
Demonstrated significant acceleration across different GPU models.
Validated the effectiveness of hardware-aware data transfer and communication optimizations.
Abstract
A high fidelity flow simulation for complex geometries for high Reynolds number () flow is still very challenging, which requires more powerful computational capability of HPC system. However, the development of HPC with traditional CPU architecture suffers bottlenecks due to its high power consumption and technical difficulties. Heterogeneous architecture computation is raised to be a promising solution of difficulties of HPC development. GPU accelerating technology has been utilized in low order scheme CFD solvers on structured grid and high order scheme solvers on unstructured meshes. The high order finite difference methods on structured grid possess many advantages, e.g. high efficiency, robustness and low storage, however, the strong dependence among points for a high order finite difference scheme still limits its application on GPU platform. In present work, we propose a set…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
