Autodesk and NVIDIA Supercharge CFD with Warp and GH200 Superchip—Simulations Just Got a Massive Upgrade

Autodesk and NVIDIA just turbocharged computational fluid dynamics—and the engineering world is taking notice.

Warp meets GH200 Superchip: A power duo for next-gen simulation

The collaboration merges Autodesk’s Warp simulation tech with NVIDIA’s GH200 Grace Hopper Superchip. The result? CFD workloads that scream through complex models with unprecedented speed. No more waiting hours for results that should take minutes.

Real-time design iteration becomes a reality

Engineers can now run high-fidelity simulations faster than ever. That means more iterations, better optimizations, and products that hit the market sooner. It’s not just about speed—it’s about smarter design, fewer physical prototypes, and a serious cut in R&D costs.

Because who has time to wait for fluid dynamics?

This isn’t just an upgrade—it’s a leap. The kind that makes older hardware look like it’s moving through molasses. And in an industry where time is money, this partnership might just be the competitive edge engineering firms didn’t know they needed.

Of course, Wall Street will still find a way to undervalue it.

Autodesk Research has taken a significant leap in computational fluid dynamics (CFD) by utilizing NVIDIA's advanced technologies, according to NVIDIA. By leveraging the Nvidia GH200 Grace Hopper Superchip and the Warp framework, Autodesk's Accelerated Lattice Boltzmann (XLB) library has achieved remarkable performance improvements, marking a pivotal moment for CFD simulations.

Advancements in CFD Performance

The integration of NVIDIA's Warp framework with the GH200 Superchip has enabled Autodesk to achieve an approximate 8x speedup in CFD simulations. This performance boost was measured against the GPU-accelerated JAX backend, showcasing the potential of Python-based solvers to match the efficiency of low-level programming languages traditionally used in CAE applications.

The Warp framework, open source and Python-based, bridges the gap between Python's accessibility and CUDA's high-performance capabilities. It allows developers to write GPU kernels directly in Python, which are then compiled to native CUDA code. This innovation facilitates the seamless integration of CFD solvers with modern AI and machine learning ecosystems.

Scalability and Efficiency

Autodesk Research's XLB library, powered by NVIDIA Warp, demonstrates near-linear scalability, handling up to 50 billion computational elements on an eight-node GH200 cluster. This scalability is achieved through an out-of-core computation strategy, where data is efficiently transferred between CPU and GPU memory, thanks to the GH200's NVLink-C2C interconnect.

The Warp framework's design allows for explicit memory management and optimized kernel programming, further enhancing memory efficiency and computational throughput. Compared to the JAX backend, Warp provides a two- to three-fold improvement in memory usage, making it a compelling choice for large-scale CFD simulations.

Bridging Performance and Productivity

The collaboration between Autodesk Research and NVIDIA highlights a new era where the historic tradeoff between development productivity and computational power is being overcome. The XLB library exemplifies how Python-native frameworks can deliver high-performance results comparable to optimized low-level code while maintaining the rapid development cycle associated with Python.

For more information on Autodesk Research's XLB library and NVIDIA's Warp framework, visit the official NVIDIA blog here.

• autodesk
• nvidia
• cfd
• python