标题： 使GPU可移植性进入Numba-JITed蒙特卡罗粒子输运代码MC/DC 显示英文标题

标题： Enabling GPU Portability into the Numba-JITed Monte Carlo Particle Transport Code MC/DC

摘要： 极大规模蒙特卡洛中子输运研究中心正在开发蒙特卡洛/动态代码（MC/DC），作为一个可在基于CPU和GPU的高性能计算机上快速探索数值方法的可移植蒙特卡洛中子输运包。在本文中，我们描述了MC/DC当前基于事件的GPU算法，以及我们在MC/DC的Python源代码中使用的即时（JIT）编译方案，以实现MC/DC在Nvidia和AMD GPU上的可操作性。为了分析性能，我们对C5G7 keigenvalue基准问题和一个连续能量无限针单元，在Nvidia Tesla V100 GPU、AMD MI250X GPU和AMD MI300A APU上进行了运行时测试，并与双插槽Intel Xeon Sapphire Rapids CPU节点进行了比较。我们发现，对于多群组C5G7基准问题，在V100、MI250X和MI300A上分别比112个Intel Xeon CPU核心获得了15$\times$、0.7$\times$、12$\times$的加速。对于连续能量无限针单元基准，我们发现在V100、MI250X和MI300A上分别比同一CPU节点获得了5$\times$、3$\times$、4$\times$的加速。 显示英文摘要

摘要： The Center for Exascale Monte Carlo Neutron Transport is developing Monte Carlo / Dynamic Code (MC/DC) as a portable Monte Carlo neutron transport package for rapid numerical methods exploration on CPU- and GPU-based high-performance computers. In this paper, we describe MC/DC's current event-based GPU algorithm as well as the just-in-time (JIT) compilation scheme we use to enable GPU operability on Nvidia and AMD GPUs from MC/DC's Python source. To analyze performance, we conduct runtime tests of the C5G7 k-eigenvalue benchmark problem and a continuous-energy infinite pin cell on Nvidia Tesla V100 GPU, AMD MI250X GPU, and the AMD MI300A APU and make comparison to a dual-socket Intel Xeon Sapphire Rapid CPU node. We found that for the multi-group C5G7 benchmark problem, we respectively see a 15$\times$, 0.7$\times$, 12$\times$ speedup on a V100, MI250X, and MI300A over 112 Intel Xeon CPU cores. For the continuous-energy infinite pin-cell benchmark, we found speedups of 5$\times$, 3$\times$, 4$\times$ on a V100, MI250X, and MI300A, respectively, over the same CPU node.