Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.23500

MadVfold: accelerating NLO event generation and reducing negative weights with SIMD vectorization and GPUs

Andrea Valassi
View original source ↗Read source document ↗Export metadata ↓

Abstract

NLO simulations are essential for LHC physics analyses but are expensive, as they are not only slow but also lead to negative weights, which imply the need to simulate much larger samples of events. Folding is a powerful technique to reduce negative weights but is itself expensive. In this paper I propose ``vectorized folding'' as a new idea to speed up these calculations using SIMD and GPUs, and I present its CUDACPP-based implementation for MG5aMC in MadVfold, including its extension for unfolded NLO event generation. Preliminary results show overall speedups around 6x to 9x with folding and 3x without it. This work is based on a test-centric, LLM-assisted software development process.

Explore related subjects

physics.comp-phhep-exhep-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Andrea Valassi. 2026-09-20. MadVfold: accelerating NLO event generation and reducing negative weights with SIMD vectorization and GPUs. https://arxiv.org/abs/2609.23500

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

A subcell-refined entropy-residual-driven limiting strategy for high-order discontinuous Galerkin methods

Fine-grained, subcell-level dissipation control is essential for achieving robust high-order discontinuous Galerkin (DG) simulations of nonlinear hyperbolic systems in under-resolved regimes while preserving accuracy. This paper proposes a subcell-refined entropy-residual-driven limiting strategy for DG on Legendre-Gauss-Lobatto nodes. The limiter introduces only nearest-neighbor pairwise dissipation within each element, with closed-form coefficients that supply the minimal dissipation required to restore the element entropy inequality. The strategy is a diagonal, locally stable approximation of classical entropy-stable methods, and a generalized subcell framework reveals split-form DG and residual-distribution-based entropy correction schemes as particular choices of the limiting coefficients. For the Euler equations, a physically consistent jump operator separately models thermal and shear entropy production while preserving velocity and pressure equilibrium; a subcell refinement of the Zhang-Shu positivity limiter ensures pointwise positivity. Extensive numerical tests confirm that the scheme maintains optimal high-order accuracy, strictly enforces entropy dissipation, and significantly reduces the difficulty of a posteriori positivity-preserving procedures.

physics.comp-ph↗
arXiv2026

VNS Tokamak for Medical Isotope Production

The Volumetric Neutron Source (VNS) tokamak is a proposed fusion reactor for testing components under fusion neutron irradiation, and has potential use for radioisotope production. The VNS geometry is modeled in the Serpent 2.2.2 and OpenMC 0.15.2 neutronics codes. Coupled neutron-photon simulations compared fluxes, spectra, and selected reaction rates in the blanket and vacuum vessel. Good agreement was found overall, with the largest difference found in (n, 2n) reactions. On an HPC cluster, Serpent 2 was found to have shorter computation time in coupled simulations, while OpenMC was faster in neutron only simulations. Radioisotope production yields were simulated in Serpent 2.2.2 for capsule and Cobalt plate irradiation facilities. Results indicate potential for large volume production of 99Mo, 131I, 225Ac, 177Lu, 192Ir, 64Cu, 67Cu, 161Tb, and 153Sm while 203Pb indicates lower potential. 100Mo and LEU target heating was calculated, suggesting the LEU target mass or the cooling may need adjustment. Optimized 60Co production yielded 1.2 GBq/mg and 100,000 TBq after a 3-year irradiation period. Sensitivity to plant outage for 99Mo, 131I, 177Lu, and 60Co was simulated, suggesting irradiation can be restarted for the same isotope loading and demonstrated long-lived 60Co to be robust to long plant dwell-time.

physics.comp-ph↗
arXiv2026

Fast and Accurate Equivariant Foundation Models for Atomistic Simulation

Machine-learned interatomic potentials (MLIPs) have emerged as a transformative tool for computational materials science and chemistry, with universal potentials trained on large and diverse datasets now routinely deployed as 'foundation models' for downstream fine-tuning in targeted chemical spaces. Many scientific applications of the resulting models, such as molecular dynamics (MD), require high inference and training speeds as well as accuracy. In this work, we examine the limits of equivariant MLIPs, which directly encode physical symmetries in model architectures, to achieve these competing targets -- particularly in the regime of extremely large datasets where data efficiency is less critical. We show how this trade-off can be addressed, and present a family of foundation potentials in the NequIP and Allegro equivariant MLIP architectures which achieve leading inference speeds and strong scalability as well as excellent accuracies across a range of community benchmarks -- spanning materials discovery, thermal conductivity prediction, and near-equilibrium mechanical and thermodynamic properties. Accelerations implemented within the NequIP infrastructure now permit training of high-accuracy foundation potentials on ultra-large datasets with dramatically reduced computational cost. Alongside, we show that efforts to improve model accuracy for materials discovery should focus on dataset diversity and improved, consistent descriptions of transition metal compound energy surfaces.

physics.comp-ph↗