Topological defects form at cosmological phase transitions by the Kibble mechanism. Cosmic strings and superstrings can lead to particularly interesting astrophysical and cosmological consequences, but this study is currently limited by the availability of accurate numerical simulations, which in turn is bottlenecked by hardware resources and computation time. Aiming to eliminate this bottleneck, in recent work we introduced and validated a GPU-accelerated evolution code for local Abelian–Higgs strings networks. While this leads to significant gains in speed, it is still limited by the physical memory available on a graphical accelerator. Here we report on a further step towards our main goal, by implementing and validating a multiple GPU extension of the earlier code, and further demonstrate its good scalability, both in terms of strong and weak scaling. A $8192^3$ production run, using 4096 GPUs, runs in 33.2 min of wall-clock time on the Piz Daint supercomputer.

拓扑缺陷是通过Kibble机理在宇宙相变过程中形成的。宇宙弦和超弦可能会导致特别令人感兴趣的天体物理学和宇宙学后果，但是当前这项研究受到精确数值模拟的限制，而后者又受到硬件资源和计算时间的限制。为了消除这一瓶颈，在最近的工作中，我们引入并验证了用于本地Abelian-Higgs字符串网络的GPU加速的演化代码。尽管这可以显着提高速度，但仍然受到图形加速器上可用物理内存的限制。在这里，我们通过实现和验证早期代码的多GPU扩展，报告了朝着我们的主要目标迈出的又一步，并进一步证明了其良好的可扩展性，无论是强扩展还是弱扩展。一种$8192^3$ 使用4096个GPU的生产运行在Piz Daint超级计算机上的挂钟时间为33.2分钟。