NVIDIA says Tensor Core FP32 methods can accelerate PET molecular dynamics while keeping energy, force and relaxation behavior close to FP32.
NVIDIA said its Tensor Core FP32 methods can speed up machine learning interatomic potentials without retraining or changing model architecture, while keeping behavior close to an FP32 baseline in the tested workloads, according to a developer blog published Oct. 29, 2026.
The post evaluates the Point Edge Transformer, or PET, a transformer-based machine learning interatomic potential developed by Lab COSMO at EPFL. NVIDIA said PET is a demanding test case because much of its runtime is already in optimized general matrix multiplications, leaving limited headroom for further optimization. The company compared BF16x9 on Blackwell GPUs and TF32x3 on Ampere GPUs, measuring kernel and end-to-end molecular dynamics performance as well as trajectory-level behavior.
For the BF16x9 path on Blackwell, NVIDIA said large PET GEMMs were accelerated by 2.2x to 2.5x on a 17,280-atom workload, while molecular-dynamics step speedups ranged from 1.2x to 1.4x on a 192-atom water box and 1.4x to 1.7x on the 17,280-atom system, depending on whether the cuBLAS strategy was "performant" or "eager." NVIDIA said the BF16x9 error was below PET's own level of non-determinism.
9x on an A100 Tensor Core GPU. 5x on the larger system, according to the post. 9 × 10⁻⁶ eV/Å.
Source link
