Exploiting Parallelism for Fast Feynman Diagrammatics

TL;DR

This paper demonstrates how leveraging GPU parallelism within the combinatorial summation framework significantly accelerates the evaluation of Feynman diagram series, enabling more efficient studies of strongly correlated quantum systems.

Contribution

It introduces a GPU-based implementation within the CoS framework to drastically speed up Feynman diagram evaluations in quantum many-body theory.

Findings

Orders of magnitude speedup in computation time.

Effective use of consumer-grade GPU hardware.

Enhanced accessibility for studying strong correlations.

Abstract

Diagrammatic expansions are a paradigmatic and powerful tool of quantum many-body theory. Their evaluation to high order, e.g., by the Diagrammatic Monte Carlo technique, can provide unbiased results in strongly correlated and challenging regimes. However, calculating a factorial number of terms to acceptable precision remains very costly even for state-of-the-art methods. We achieve a dramatic acceleration of evaluating Feynman's diagrammatic series by use of specialised hardware architecture within the recently introduced combinatorial summation (CoS) framework. We present how exploiting the massive parallelism and concurrency available from GPUs leads to orders of magnitude improvement in computation time even on consumer-grade hardware. This provides a platform for making probes of novel phenomena of strong correlations much more accessible.