torchode: A Parallel ODE Solver for PyTorch
Department of Informatics & Munich Data Science Institute, Technical University of Munich, Germany
arXiv:2210.12375 [cs.LG], 22 Oct 2022
@misc{https://doi.org/10.48550/arxiv.2210.12375,
doi={10.48550/ARXIV.2210.12375},
url={https://arxiv.org/abs/2210.12375},
author={Lienen, Marten and Günnemann, Stephan},
keywords={Machine Learning (cs.LG), Numerical Analysis (math.NA), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Mathematics, FOS: Mathematics},
title={torchode: A Parallel ODE Solver for PyTorch},
publisher={arXiv},
year={2022},
copyright={arXiv.org perpetual, non-exclusive license}
}
We introduce an ODE solver for the PyTorch ecosystem that can solve multiple ODEs in parallel independently from each other while achieving significant performance gains. Our implementation tracks each ODE’s progress separately and is carefully optimized for GPUs and compatibility with PyTorch’s JIT compiler. Its design lets researchers easily augment any aspect of the solver and collect and analyze internal solver statistics. In our experiments, our implementation is up to 4.3 times faster per step than other ODE solvers and it is robust against within-batch interactions that lead other solvers to take up to 4 times as many steps.
October 30, 2022 by hgpu
