We present torchami, an advanced implementation of algorithmic Matsubara integration (AMI) that utilizes pytorch as a backend to provide easy parallelization and GPU support. AMI is a tool for analytically resolving the sequence of nested Matsubara integrals that arise in virtually all Feynman perturbative expansions. In this implementation we present a new AMI algorithm that creates a more natural symbolic representation of the Feynman integrands. In addition, we include peripheral tools that allow for import and labelling of simple graph structures and conversion to torchami input. The code is written in c++ with python bindings provided.
Cite
@article{arxiv.2311.17189,
title = {TorchAmi: Generalized CPU/GPU Implementation of Algorithmic Matsubara Integration},
author = {M. D. Burke and J. P. F. LeBlanc},
journal= {arXiv preprint arXiv:2311.17189},
year = {2023}
}