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arXiv 2602.09111hep-ph
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多事件接口用于MadGraph5_aMC@NLO中的次领次序计算

A multi-event interface for next-to-leading order calculations in MadGraph5_aMC@NLO

Lund University(隆德大学);European Organization for Nuclear Research(欧洲核子研究中心);Marietta Blau Institute for Particle Physics, Austrian Academy of Sciences(奥地利科学院玛丽亚·布劳粒子物理研究所);Università degli Studi di Milano & INFN Sezione di Milano(米兰大学与意大利国家核物理研究所米兰分部)

Rikkert Frederix, Stefan Roiser, Robert Schöfbeck, Zenny Wettersten, Marco Zaro

AI总结:

MadGraph5_aMC@NLO实现了多事件接口以支持次领次序计算,通过并行处理树级散射幅提升QCD事件生成效率。

摘要:

我们详细介绍了MadGraph5_aMC@NLO中次领次序(NLO)计算的多事件接口的实现,该接口允许在每次调用整合的NLO微分截面进行事件生成时,对多个相空间点的树级散射幅进行评估。此外,基于此多事件接口的多线程实现也被提出,其中树级幅在多个CPU线程中并行计算,用于量子色动力学(QCD)事件的蒙特卡洛生成。虽然这项工作主要涉及实现的代码,但还包括一些涉及相空间切口应用顺序和不同散射幅调用顺序的算法变化。当前的代码库支持多线程执行,但这些变化为继续以CPU SIMD指令或SIMT GPU卸载形式实现的数据并行化铺平了道路。对不同过程中不同图示贡献所花费的运行时间进行研究表明,NLO QCD事件生成在计算上主要由树级散射幅评估主导,我们显示这些评估完美适合数据并行化。

英文摘要:

We detail the implementation of a multi-event interface for next-to-leading order (NLO) calculations in MadGraph5_aMC@NLO, allowing tree-level scattering amplitudes for multiple phase space points to be evaluated in each call to the integrated NLO differential cross section during event generation. Additionally, a multithreaded implementation based on this multi-event interface where tree-level amplitudes are evaluated in parallel across multiple CPU threads is presented for the Monte Carlo generation of quantum chromodynamical (QCD) events. Although this work primarily concerns the implemented code, some algorithmic changes involving the order of the application of phase-space cuts and calls to different scattering amplitudes are included. The codebase currently supports multi-threaded execution, but these changes pave the way for continued data parallelism in the form of on-CPU SIMD instructions or SIMT GPU offloading. A study in the runtime fraction spent in different diagrammatic contributions across various processes suggests that NLO QCD event generation are computationally dominated by tree-level scattering amplitude evaluations, which we show are perfectly suited for data parallelisation.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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