GPU ACCELERATION FOR CAE
Supercharge CAE Simulations with GPU Acceleration
Harness the performance of next-generation GPUs to tackle the most demanding Computer-Aided Engineering (CAE) challenges by leveraging the power of parallel computing. By shifting computationally intensive tasks from CPUs to GPUs, Rescale unlocks order-of-magnitude improvements in performance and cost efficiency for a wide range of applications.
Untapped Performance and Hardware Delays Stall Innovation Speed
CPU-Bound Workloads
GPU-eligible simulation jobs still run on CPUs, leaving major speed and cost-efficiency gains completely untapped.
Hardware Access Gaps
Long procurement cycles and scarce GPU supply keep teams from the architectures their workloads need.
Visualization Friction
Pre- and post-processing work lacks the GPU-backed virtual desktops engineers need for fast, interactive iteration.
Achieve Breakthrough Speed and Cost-Efficiency
Modernize CPU-Bound Workloads
Shift GPU-eligible simulations onto benchmarked, production-ready environments and capture the gains already proven for your applications.
Secure GPU Capacity On Demand
Reach the latest architectures across major and specialty clouds, with reserved options that keep capacity available as demand climbs.
Support Compute and Visualization
Accelerate batch solving and interactive pre- and post-processing together, so engineers stay productive end to end.
Scalable GPU Performance Built for Engineering
30x
Greater cost-efficiency running CFD on GPUs versus CPUs
Supersonic aircraft manufacturer
95%+
Reduction in cost per job in a multi-GPU environment
Energy equipment manufacturer
2x
Faster results for molecular dynamics and drug discovery workloads
Life sciences biotech company
The Best Way To Explore Rescale Is Hands On.
GPU Acceleration Capabilities
GPU-OPtimized CAE
Harness the Power of Compute Parallelization
GPUs significantly enhance high performance computing (HPC) by accelerating computationally intensive workloads. Originally designed for graphics rendering, GPUs excel at parallel processing, making them ideal for simulations, data analytics, and AI/ML. Rescale makes this power accessible for both CAE and other computationally intensive tasks, delivering substantial performance gains.
VISUALIZATION & VIRTUAL DESKTOPS
Visualize Large-Scale Simulations in Real Time
Rescale’s Elastic Cloud Workstations, virtual desktops tailored to the needs of HPC and AI workflows, provide engineers with the ability to interact with complex models and simulations in real time. Access to the latest GPUs on Rescale is crucial for pre-processing, post-processing, and interactive simulation, enabling faster analysis and better-informed decisions.
New GPU Hardware Adoption
Stay at the Cutting Edge of HPC and AI Compute Power
Rescale provides access to the latest GPU architectures—including those from NVIDIA, such as the B200—along with NVIDIA’s CUDA-X libraries and Omniverse platform for advanced engineering use cases. This ensures that users can leverage the most advanced hardware for their CAE workflows, maximizing performance and staying competitive.
SPECIALTY CLOUDS FOR GPU CAPACITY
Access GPUs at Any Scale
Rescale offers scalable access to GPU resources across major cloud providers and specialized cloud solutions such as CoreWeave. To ensure availability of GPUs as demand increases, Rescale also provides reserved capacity purchasing options.
CFD GPU Benchmark
Ansys Fluent Accelerated
Ansys Fluent, a widely used Computational Fluid Dynamics (CFD) solver in industries like aerospace, automotive, and energy, benefits significantly from GPU acceleration. Fluent’s GPU solver delivers a substantial performance increase compared to its CPU-based counterpart, as demonstrated by external aerodynamics benchmarks where lower solve times indicate superior performance.
CFD GPU Benchmark
Siemens STAR-CCM+ Cost-Optimized
Siemens Simcenter STAR-CCM+ is a comprehensive multiphysics simulation tool used to analyze fluid flow, heat transfer, and structural mechanics in complex geometries. Its GPU-accelerated solver delivers significant performance gains over traditional CPU-based approaches, as shown in benchmark comparisons where lower costs and solve times highlight improvements in both speed and energy efficiency.
Optical Benchmark
Ansys Speos Solve-Time Reduced
Ansys Speos is used to predict illumination and optical performance, reducing the need for physical prototypes while improving design efficiency. The GPU-accelerated solver in Ansys Speos delivers improved performance compared to CPU-based solutions, speeding up the design and analysis of optical systems.
Explore GPU Acceleration Resources
Leveraging Specialized Architectures with Domain-Specific Hardware Accelerators: Nvidia GPUs and Arm Chips on Rescale
Harnessing the Power of the Cloud for Your Heterogeneous Computational Workflows
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Rescale at GTC 2025: AI and GPUs' Impact on Computational Engineering and HPC
Summary of key announcements, trends, and news for computational engineers and technology leaders in HPC and AI
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A Guide to GPU-Accelerated CAE and the Cost-Performance Benefits
GPU-accelerated computing is one of the most transformative trends in modeling and simulation today. All of the major engineering software providers are supporting GPU architectures in addition to traditional CPUs,…
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