A Guide to GPU-Accelerated CAE and the Cost-Performance Benefits

GPU-accelerated computing is revolutionizing modeling and simulation in engineering by significantly enhancing performance and reducing costs in various applications such as finite element analysis, computational fluid dynamics, and electromagnetic analysis. Major software providers including Ansys, Altair, and Siemens are integrating GPU support through partnerships with NVIDIA, achieving speed improvements up to 20 times compared to traditional CPU-based methods. GPU acceleration divides into graphics and solver acceleration, enhancing tasks like visual rendering and complex computational workloads. While consumer GPUs might not be suitable for high-performance computing (HPC), specialized GPUs like NVIDIA's V100 and A100 that support double precision and high memory bandwidth are crucial for accurate and efficient simulations. The widespread adoption of GPU acceleration enables complex multi-physics simulations that were previously unfeasible, such as NASA's detailed modeling of aerodynamics and heat transfer. As demand for high-end GPUs grows, strategic planning is essential for optimizing engineering workloads, with companies like Rescale providing expertise in configuring the most efficient GPU setups for both performance and cost-effectiveness.