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Computational challenges for MC event generation

Andy Buckley
(
- Glasgow U.
)

Jul 31, 2019

15 pages

Part of Proceedings, 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research: Empowering the revolution: Bringing Machine Learning to High Performance Computing (ACAT 2019) : Saas-Fee, Switzerland, March 10-15, 2019

Published in:

J.Phys.Conf.Ser. 1525 (2020) 1, 012023

Contribution to:

- ACAT 2019

Published: Jul 8, 2020

e-Print:

1908.00167 [hep-ph]

DOI:

10.1088/1742-6596/1525/1/012023

Report number:

MCnet-19-18

View in:

ADS Abstract Service

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Abstract: (arXiv)

The sophistication of fully exclusive MC event generation has grown at an extraordinary rate since the start of the LHC era, but has been mirrored by a similarly extraordinary rise in the CPU cost of state-of-the-art MC calculations. The reliance of experimental analyses on these calculations raises the disturbing spectre of MC computations being a leading limitation on the physics impact of the HL-LHC, with MC trends showing more signs of further cost-increases rather than the desired speed-ups. I review the methods and bottlenecks in MC computation, and areas where new computing architectures, machine-learning methods, and social structures may help to avert calamity.

Note:

Based on a plenary talk at ACAT 2019

CERN LHC Coll: upgrade
structure
field theory
scattering
higher-order: 1
Monte Carlo
computer: algebra
numerical methods

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