Feynman integral reduction: balanced reconstruction of sparse rational functions and implementation on supercomputers in a co-design approach
Alexander Smirnov, Mao Zeng

TL;DR
This paper introduces a sparse rational function reconstruction method optimized for supercomputers, enhancing the efficiency of IBP reduction in Feynman integral calculations through co-designed algorithms and infrastructure.
Contribution
It presents a novel balanced reconstruction technique combining sparsity exploitation and parallel probing, tailored for high-performance supercomputing environments in quantum field theory computations.
Findings
Achieved efficient IBP reductions for complex integrals on supercomputers.
Demonstrated significant resource savings with the new reconstruction method.
Validated approach with benchmarks on realistic and synthetic problems.
Abstract
Integration-by-parts (IBP) reduction is one of the essential steps in evaluating Feynman integrals. A modern approach to IBP reduction uses modular arithmetic evaluations with parameters set to numerical values at sample points, followed by reconstruction of the analytic rational coefficients. Due to the large number of sample points needed, problems at the frontier of science require an application of supercomputers. In this article, we present a rational function reconstruction method that fully takes advantage of sparsity, combining the balanced reconstruction method and the Zippel method. Additionally, to improve the efficiency of the finite-field IBP reduction runs, at each run several numerical probes are computed simultaneously, which allows to decrease the resource overhead. We describe what performance issues one encounters on the way to an efficient implementation on…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNumerical Methods and Algorithms · Computational Physics and Python Applications · Algebraic and Geometric Analysis
