Presentation
TorchFort: A Library for Online Deep Learning in Fortran HPC Programs
Presenter
DescriptionDeep learning has shown promise in reducing computational cost or as an alternative method for modeling physical phenomena for a broad range of scientific applications. In these domains, the data sources are numerical simulation programs typically implemented in C, C++, or still often, Fortran. This is in contrast to popular deep learning frameworks that users interact with using Python. A source of friction that often arises is how to efficiently couple the simulation program with the DL framework for training or inference.
In this talk, we discuss TorchFort, a library for online DL training and inference implemented with LibTorch, the C++ backend used by PyTorch. This library can be invoked directly from Fortran/C/C++, enabling transparent sharing of data arrays from the simulation program to the DL framework, all contained within the simulation process. We will talk about the library design and some implementation examples to present opportunities this tight coupling presents for DL applications.
In this talk, we discuss TorchFort, a library for online DL training and inference implemented with LibTorch, the C++ backend used by PyTorch. This library can be invoked directly from Fortran/C/C++, enabling transparent sharing of data arrays from the simulation program to the DL framework, all contained within the simulation process. We will talk about the library design and some implementation examples to present opportunities this tight coupling presents for DL applications.
TimeTuesday, June 417:00 - 17:30 CEST
LocationHG E 3
Session Chair
Event Type
Minisymposium
Chemistry and Materials
Climate, Weather, and Earth Sciences
Engineering
Computational Methods and Applied Mathematics
