可扩展的域分解蒙特卡罗中性输运用于核聚变
Scalable Domain-decomposed Monte Carlo Neutral Transport for Nuclear Fusion
AI总结：
本文提出一种域分解蒙特卡罗算法，用于解决网格数据无法单节点处理的问题，通过强标度测试显示该算法在多数情况下性能优于现有方法，且在超算上实现了高效的并行扩展。
摘要：
EIRENE [1] 是一种广泛用于等离子体物理领域的蒙特卡罗中性输运求解器。EIRENE 未实现域分解，因此无法用于网格数据无法单节点处理的模拟（例如见[2]）。本文提出了一种域分解蒙特卡罗（DDMC）算法，该算法在新的开源蒙特卡罗代码 Eiron 中实现。EIRENE 中目前使用的两种并行算法也已在 Eiron 中实现，三种算法通过强标度测试进行比较，结果显示 DDMC 在几乎所有情况下均优于其他两种算法。在超算 Mahti [3] 上，对于无法放入 L3 缓存切片（4 MiB）的网格，DDMC 的强标度是超线性的。在弱标度测试中，DDMC 被扩展到 16384 个核心，高碰撞（更重的计算负载）情况下弱标度效率为 45%，低碰撞（较轻的计算负载）情况下为 26%。我们得出结论，将此域分解算法实现于 EIRENE 中将提高性能，并使目前由于内存限制无法进行的模拟成为可能。
英文摘要：
EIRENE [1] is a Monte Carlo neutral transport solver heavily used in the fusion community. EIRENE does not implement domain decomposition, making it impossible to use for simulations where the grid data does not fit on one compute node (see e.g. [2]). This paper presents a domain-decomposed Monte Carlo (DDMC) algorithm implemented in a new open source Monte Carlo code, Eiron. Two parallel algorithms currently used in EIRENE are also implemented in Eiron, and the three algorithms are compared by running strong scaling tests, with DDMC performing better than the other two algorithms in nearly all cases. On the supercomputer Mahti [3], DDMC strong scaling is superlinear for grids that do not fit into an L3 cache slice (4 MiB). The DDMC algorithm is also scaled up to 16384 cores in weak scaling tests, with a weak scaling efficiency of 45% in a high-collisional (heavier compute load) case, and 26% in a low-collisional (lighter compute load) case. We conclude that implementing this domain decomposition algorithm in EIRENE would improve performance and enable simulations that are currently impossible due to memory constraints.
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。
