MadVfold：利用SIMD向量化与GPU加速NLO事件生成并减少负权重
MadVfold: accelerating NLO event generation and reducing negative weights with SIMD vectorization and GPUs
- CERN, Experimental Physics Department（欧洲核子研究中心实验物理部）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文提出向量化折叠技术，利用SIMD和GPU在MadVfold中加速NLO事件生成并减少负权重，初步实现6-9倍加速。
中文摘要 AI 辅助
NLO模拟对于LHC物理分析至关重要，但代价高昂，因为它们不仅速度慢，还会导致负权重，这意味着需要模拟更大规模的事件样本。折叠是一种减少负权重的强大技术，但其本身也很昂贵。在本文中，我提出“向量化折叠”作为利用SIMD和GPU加速这些计算的新思路，并展示了其在MadVfold中基于CUDACPP的MG5aMC实现，包括其对非折叠NLO事件生成的扩展。初步结果显示，在折叠情况下总体加速约6倍至9倍，无折叠时约3倍。这项工作基于以测试为中心、LLM辅助的软件开发流程。
英文摘要
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.
