Accelerating Computational Fluid Dynamics with A Multi-GPU Solver for Large Scale Simulations

GPUs are transforming scientific computing by delivering substantial speedups and energy savings. This presentation outlines the development of GPU computing strategies for the leading CFD software, Ansys Fluent. Initially, we identified and optimized computational bottlenecks suitable for GPU acceleration, using an offload model. Later, to overcome limitations imposed by Amdahl's law, we developed a GPU-native solver architecture with streamlined APIs, ensuring seamless integration with existing workflows. Our optimization strategy accounts for diverse GPU platforms, implementing platform specific enhancements for NVIDIA, AMD, and Intel architectures. Scalability is achieved through advanced load-balancing algorithms and improved inter-GPU communication, enabling efficient parallelization for large-scale simulations. We present results demonstrating significant speedups and energy savings compared to CPU-based codes, highlighting the transformative potential of GPUs in enabling faster and more complex simulations.