S. Borowka
A GPU compatible quasi-Monte Carlo integrator interfaced to pySecDec
Borowka, S.; Heinrich, G.; Jahn, S.; Jones, S.P.; Kerner, M.; Schlenk, J.
Authors
G. Heinrich
S. Jahn
S.P. Jones
M. Kerner
J. Schlenk
Abstract
The purely numerical evaluation of multi-loop integrals and amplitudes can be a viable alternative to analytic approaches, in particular in the presence of several mass scales, provided sufficient accuracy can be achieved in an acceptable amount of time. For many multi-loop integrals, the fraction of time required to perform the numerical integration is significant and it is therefore beneficial to have efficient and well-implemented numerical integration methods. With this goal in mind, we present a new stand-alone integrator based on the use of (quasi-Monte Carlo) rank-1 shifted lattice rules. For integrals with high variance we also implement a variance reduction algorithm based on fitting a smooth function to the inverse cumulative distribution function of the integrand dimension-by-dimension. Additionally, the new integrator is interfaced to pySecDec to allow the straightforward evaluation of multi-loop integrals and dimensionally regulated parameter integrals. In order to make use of recent advances in parallel computing hardware, our integrator can be used both on CPUs and CUDA compatible GPUs where available.
Citation
Borowka, S., Heinrich, G., Jahn, S., Jones, S., Kerner, M., & Schlenk, J. (2019). A GPU compatible quasi-Monte Carlo integrator interfaced to pySecDec. Computer Physics Communications, 240, 120-137. https://doi.org/10.1016/j.cpc.2019.02.015
Journal Article Type | Article |
---|---|
Acceptance Date | Feb 27, 2019 |
Publication Date | Jul 31, 2019 |
Deposit Date | Mar 27, 2019 |
Publicly Available Date | Jan 23, 2020 |
Journal | Computer Physics Communications |
Print ISSN | 0010-4655 |
Electronic ISSN | 1879-2944 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 240 |
Pages | 120-137 |
DOI | https://doi.org/10.1016/j.cpc.2019.02.015 |
Public URL | https://durham-repository.worktribe.com/output/1305254 |
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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