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Computational challenges for MC event generation
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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.
Forward citations
Cited by 3 Pith papers
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Accelerating Berends-Giele recursion for gluons in arbitrary dimensions over finite fields
A publicly available GPU implementation of Berends-Giele recursion computes pure gluon amplitudes in arbitrary spacetime dimensions over finite fields.
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A Demonstration of ARCANE Reweighting: Reducing the Sign Problem in the MC@NLO Generation of $e^+ e^- \rightarrow q \bar{q} + 1\, jet$ Events
ARCANE reweighting cuts the post-unweighting negative-event fraction in e+e- -> q qbar + 1 jet MC@NLO generation from about 2.25% to below 10^-5 while preserving the visible event distributions.
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ARCANE Reweighting: A Monte Carlo Technique to Tackle the Negative Weights Problem in Collider Event Generation
ARCANE reweighting adds a carefully designed, zero-average correction to event weights so that positive and negative pathways to the same event cancel, preserving all physical distributions.
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