Taking Computational Fluid Dynamics to the Next Level with the NVIDIA H200 Tensor Core GPU

Summary

Computational Fluid Dynamics (CFD) is a critical tool in various industries and academic fields, used to simulate fluid flow and related phenomena. The NVIDIA H200 Tensor Core GPU marks a significant advancement in CFD performance, thanks to its 140 GB of HBM3e memory and 4.8 TB/s of memory bandwidth. This article explores how the H200 GPU enhances CFD simulations, comparing its performance to previous generations and highlighting its benefits in running larger, more complex models.

Taking Computational Fluid Dynamics to New Heights with NVIDIA H200 Tensor Core GPU

Computational Fluid Dynamics (CFD) is a powerful simulation tool used across industries and academia to study fluid flow and related phenomena. It plays a crucial role in designing and optimizing products, from aircraft and cars to buildings and industrial machinery. However, CFD simulations are computationally intensive and require high-performance computing (HPC) to achieve practical results within a reasonable timeframe.

The Role of GPUs in CFD

Graphics Processing Units (GPUs) have emerged as a game-changer for CFD simulations. Unlike Central Processing Units (CPUs), GPUs are designed to handle parallel computations, making them ideal for the complex calculations involved in CFD. The NVIDIA H200 Tensor Core GPU represents a significant leap forward in this area.

Key Features of the NVIDIA H200 Tensor Core GPU

• Memory and Bandwidth: The H200 GPU boasts 140 GB of HBM3e memory and 4.8 TB/s of memory bandwidth. This is a substantial improvement over previous generations, such as the A100 and H100 GPUs, which had 80 GB of memory and lower bandwidth.
• Performance: The increased memory and bandwidth enable the H200 GPU to run larger, more complex CFD models. For instance, while an 80-GB GPU can handle a case of approximately 90 million cells, the H200 GPU can manage cases closer to 160 million cells.

Comparative Performance

To demonstrate the performance of the H200 GPU, NVIDIA partnered with Ansys to test the industry-standard Ansys Fluent CFD solver. The results were impressive:

• GPU vs. CPU Performance: Eight H200 GPUs were found to be 34 times faster than 512 CPU cores. Even 96 CPU nodes could not match the performance of eight H200 GPUs, which were still 1.9 times faster.
• Generational Improvement: Comparing the performance of different GPU generations, eight H200 GPUs were 1.9 times faster than eight A100 GPUs for the same 250 million-cell DrivAer model case.

Benefits of Enhanced CFD Performance

The advancements in CFD performance offered by the H200 GPU have several benefits:

• Larger, Higher Fidelity Models: The increased memory and bandwidth allow for the simulation of larger, more complex models, which can lead to better product performance.
• Reduced Simulation Time: The improved performance means that transient, scale-resolved cases that previously took weeks to complete on CPUs can now be finished in hours on GPUs.
• Economic Advantages: GPU-based CFD simulations offer significant cost savings compared to CPU clusters with comparable performance.

Table 1: Comparison of Memory and Memory Bandwidth for NVIDIA GPUs

Figure 1: Performance Comparison of Ansys Fluent on NVIDIA GPUs and CPUs

The blue line shows CPU performance from 4 to 96 nodes (512 to 12288 cores), while the red line represents the performance of eight H200 GPUs.

Figure 2: Generational Performance Improvement

The graph compares the performance of eight H200 GPUs with eight A100 GPUs for the 250 million-cell DrivAer model case.

Conclusion

The NVIDIA H200 Tensor Core GPU represents a significant step forward in CFD performance, enabling larger, more complex simulations to be run faster and more efficiently. With its enhanced memory and bandwidth, the H200 GPU offers substantial improvements over previous generations, making it a powerful tool for industries and academia relying on CFD simulations.