CUBE --宇宙学N体模拟的最佳标度
CUBE -- Towards an Optimal Scaling of Cosmological N-body Simulations
摘要：
N体模拟是物理宇宙学中理解宇宙大尺度结构（LSS）形成的重要工具。高分辨率的大尺度模拟对于探索宇宙的亚结构和确定中微子质量等基本物理参数具有重要意义。然而，传统的基于粒子网格（PM）的算法使用大量的内存，这限制了模拟的可扩展性。因此，我们设计了一个两级PM算法CUBE对最佳性能的内存消耗减少。通过使用定点压缩技术，CUBE将每个N体粒子的内存消耗减少到6字节，比传统的基于PM的算法低一个数量级。我们在基于Intel Cascade Lake的超级计算机上将CUBE扩展到512个节点（20，480个核心），弱扩展效率为$\simeq$ 95\%。这个尺度测试是在“宇宙- $π$“中进行的--这是一个宇宙学LSS模拟，使用$\simeq$ 4. 4万亿个粒子，追踪宇宙在$\simeq$ 137亿年中的演化。据我们所知，Cosmo- $π$是迄今为止完成的最大的宇宙学N体模拟。我们相信，CUBE具有巨大的潜力，可以在exascale超级计算机上进行更大规模的模拟。
英文摘要：
N-body simulations are essential tools in physical cosmology to understand the large-scale structure (LSS) formation of the Universe. Large-scale simulations with high resolution are important for exploring the substructure of universe and for determining fundamental physical parameters like neutrino mass. However, traditional particle-mesh (PM) based algorithms use considerable amounts of memory, which limits the scalability of simulations. Therefore, we designed a two-level PM algorithm CUBE towards optimal performance in memory consumption reduction. By using the fixed-point compression technique, CUBE reduces the memory consumption per N-body particle toward 6 bytes, an order of magnitude lower than the traditional PM-based algorithms. We scaled CUBE to 512 nodes (20,480 cores) on an Intel Cascade Lake based supercomputer with $\simeq$95\% weak-scaling efficiency. This scaling test was performed in "Cosmo-$π$" -- a cosmological LSS simulation using $\simeq$4.4 trillion particles, tracing the evolution of the universe over $\simeq$13.7 billion years. To our best knowledge, Cosmo-$π$ is the largest completed cosmological N-body simulation. We believe CUBE has a huge potential to scale on exascale supercomputers for larger simulations.
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