Monte Carlo (MC) simulations play a pivotal role in diverse scientific and engineering domains, with applications ranging from nuclear physics to materials science. Harnessing the computational power of high-performance computing (HPC) systems, especially Graphics Processing Units (GPUs), has become essential for accelerating MC simulations. This paper focuses on the adaptation and optimization of the OpenMC neutron and photon transport Monte Carlo code for Intel GPUs, specifically the Intel Data Center Max 1100 GPU (codename Ponte Vecchio, PVC), through distributed OpenMP offloading. Building upon prior work by Tramm J.R., et al. (2022), which laid the groundwork for GPU adaptation, our study meticulously extends the OpenMC code's capabilities to Intel GPUs. We present a comprehensive benchmarking and scaling analysis, comparing performance on Intel MAX GPUs to state-of-the-art CPU execution (Intel Xeon Platinum 8480+ Processor, codename 4th generation Sapphire Rapids). The results demonstrate a remarkable acceleration factor compared to CPU execution, showcasing the GPU-adapted code's superiority over its CPU counterpart as computational load increases.

翻译：蒙特卡洛（MC）模拟在核物理、材料科学等多个科学与工程领域发挥着关键作用。充分利用高性能计算（HPC）系统，特别是图形处理器（GPU）的计算能力，已成为加速MC模拟的关键。本文聚焦于面向英特尔GPU（具体为英特尔数据中心Max 1100 GPU，代号Ponte Vecchio，简称PVC）对OpenMC中子与光子输运蒙特卡洛代码进行适配与优化，通过分布式OpenMP卸载技术实现。基于Tramm J.R.等人（2022年）奠定的GPU适配基础，本研究将OpenMC代码的功能精细扩展至英特尔GPU。我们开展了全面的基准测试与扩展性分析，将英特尔MAX GPU上的性能与最先进的CPU执行性能（英特尔至强铂金8480+处理器，代号第四代Sapphire Rapids）进行对比。结果表明，与CPU执行相比，GPU适配代码在计算负载增加时展现出显著的加速比优势。