AI 中文总结
GRACE是用于GPU加速数值相对论的开源框架，基于开源库开发，采用Kokkos和p4est实现性能可移植性与自适应网格细化。经多类标准测试验证，演化双中子星合并案例，报告了相关性能结果，还与GRACEpy一同发布。
AI 中文摘要
我们展示了GRACE，一个新的用于GPU加速的数值相对论框架，旨在在异构高性能计算平台上高效运行。它完全基于开源库从头开发，采用Kokkos实现跨CPU和GPU架构的性能可移植性，用p4est进行自适应网格细化。代码在固定或自适应细化网格上自洽地演化理想GRMHD方程，并与Z4c形式的爱因斯坦方程耦合。我们通过一系列标准测试验证了该实现，还演化了两个双中子星合并案例。最后报告了单设备吞吐量以及在多个GPU和CPU架构上的强缩放和弱缩放结果。GRACE与GRACEpy一起公开发布，GRACEpy是一个基本的后处理和数据分析环境。
英文摘要
We present GRACE, a new GPU-accelerated numerical-relativity framework designed to run efficiently on heterogeneous high-performance computing platforms. Developed from scratch and built exclusively on open-source libraries, GRACE employs Kokkos for performance portability across CPU and GPU architectures and p4est for adaptive mesh refinement. The code evolves the equations of ideal GRMHD -- with divergence-free magnetic fields maintained by constrained transport -- self-consistently coupled to the Einstein equations in the Z4c formulation, on fixed or adaptively refined grids. We validate the implementation against a suite of standard tests, ranging from magnetized shock tubes and the magnetic rotor in flat spacetime, through (magnetized) Bondi accretion onto a Schwarzschild black hole and the ringdown of a perturbed spinning puncture, to neutron-star oscillation spectra in fixed and dynamical spacetimes and the merger of binary black holes. As more demanding applications, we evolve two binary neutron-star mergers -- an equal-mass, unmagnetized system with an ideal-gas equation of state and an unequal-mass, magnetized system with a finite-temperature tabulated equation of state -- finding the inspiral dynamics to agree well with the FIL code. We also report single-device throughput together with strong- and weak-scaling results on multiple GPU and CPU architectures. GRACE is publicly released together with GRACEpy, a basic post-processing and data-analysis environment.
