GPU Acceleration
Graphics processors run thousands of simple operations in parallel, accelerating the regular arithmetic common in simulation and learning.
A different kind of processor
A central processing unit (CPU) has a few powerful cores optimized for varied, branching work. A graphics processing unit (GPU) has thousands of simpler cores optimized to apply the same operation to many data elements at once. For the right problems, this delivers a large speedup.
Where GPUs shine
- Dense linear algebra: matrix multiplications at the heart of many solvers.
- Grid-based physics where each cell updates the same way.
- Neural network training and inference.
The memory reality
A GPU has its own memory, and moving data between CPU and GPU is slow relative to computation. Effective GPU code minimizes these transfers and keeps data resident on the device. Many naive ports run slower than the CPU version purely because of transfer overhead.
Precision trade-offs
GPUs often run fastest in lower precision. That is acceptable for some machine-learning work but dangerous for physics where accuracy and reproducibility matter. The precision used must match the requirements of the result, not just the speed of the hardware.
Fit to fusion computing
Physics surrogates, turbulence-adjacent kernels, and design-space sweeps all contain the regular, parallel arithmetic GPUs accelerate well. Used deliberately, GPUs shorten the time to evaluate design options for machines like the breeder Hyperion — provided results are verified against trusted CPU baselines.