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20122026
most citedParton distributions for the LHC Run II

2.6k citations · 12.9k across the 87 of their papers we have counts for

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8 papers · 1 filter

physics.comp-ph2022

Extending MadFlow: device-specific optimization

Stefano Carrazza, Juan M. Cruz-Martinez, Gabriele Palazzo

In this proceedings we demonstrate some advantages of a top-bottom approach in the development of hardware-accelerated code. We start with an autogenerated hardware-agnostic Monte…

physics.comp-ph2021★ 14 cited

MadFlow: automating Monte Carlo simulation on GPU for particle physics processes

Stefano Carrazza, Juan Cruz-Martinez, Marco Rossi +1

We present MadFlow, a first general multi-purpose framework for Monte Carlo (MC) event simulation of particle physics processes designed to take full advantage of hardware accelera…

physics.comp-ph2021★ 9 cited

Towards the automation of Monte Carlo simulation on GPU for particle physics processes

Stefano Carrazza, Juan Cruz-Martinez, Marco Rossi +1

In this proceedings we present MadFlow, a new framework for the automation of Monte Carlo (MC) simulation on graphics processing units (GPU) for particle physics processes. In orde…

physics.comp-ph2020★ 11 cited

MCNNTUNES: tuning Shower Monte Carlo generators with machine learning

Marco Lazzarin, Simone Alioli, Stefano Carrazza

The parameters tuning of event generators is a research topic characterized by complex choices: the generator response to parameter variations is difficult to obtain on a theoretic…

physics.comp-ph2020★ 3 cited

VegasFlow: accelerating Monte Carlo simulation across platforms

Juan M. Cruz-Martinez, Stefano Carrazza

In this work we demonstrate the usage of the VegasFlow library on multidevice situations: multi-GPU in one single node and multi-node in a cluster. VegasFlow is a new software for…

physics.comp-ph2020★ 36 cited

VegasFlow: accelerating Monte Carlo simulation across multiple hardware platforms

Stefano Carrazza, Juan M. Cruz-Martinez

We present VegasFlow, a new software for fast evaluation of high dimensional integrals based on Monte Carlo integration techniques designed for platforms with hardware accelerators…