A GPU-Accelerated AMR Solver for Gravitational Wave Propagation
A GPU-Accelerated AMR Solver for Gravitational Wave Propagation
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DOI：
10.1109/sc41404.2022.00080
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Milinda Fernando;D. Neilsen;E. Hirschmann;Y. Zlochower;H. Sundar;O. Ghattas;G. Biros
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文献类型：
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作者：
Milinda Fernando;D. Neilsen;E. Hirschmann;Y. Zlochower;H. Sundar;O. Ghattas;G. Biros
计算单个重力波形 (GW) 的模拟可能需要数周时间。然而，引力波的探测和解释需要数千次这样的模拟。未来的探测器将需要比目前使用的探测器更精确的波形。我们在此展示第一个大规模、自适应网格、多 GPU 数值相对论 (NR) 代码以及性能分析和基准测试。虽然很难进行比较，但我们的 Dendro-grNr 代码的 GPU 扩展比现有最先进的代码实现了 6 倍的加速。我们在单个 NVIDIA A100 GPU 上实现了 800 GFlops/s，与具有同等 CPU 实现的两路 128 核 AMD EPYC 7763 CPU 节点相比，整体加速提高了 2.5 倍。我们提供了针对质量比 $\mathrm{q}=1,2,4$ 计算的 GW 的详细性能分析、并行可扩展性结果和准确性评估。我们还在德克萨斯州高级计算中心的 Frontera 系统上提供了高达 8 个 A100 的强大可扩展性和高达 229,376 个 x86 内核的弱扩展性。
Simulations to calculate a single gravitational waveform (GW) can take several weeks. Yet, thousands of such simulations are needed for the detection and interpretation of gravitational waves. Future detectors will require even more accurate waveforms than those currently used. We present here the first large scale, adaptive mesh, multi-GPU numerical relativity (NR) code together with performance analysis and benchmarking. While comparisons are difficult to make, our GPU extension of the Dendro-grNr code achieves a 6x speedup over existing state-of-the-art codes. We achieve 800 GFlops/s on a single NVIDIA A100 GPU with an overall 2.5x speedup over a two-socket, 128-core AMD EPYC 7763 CPU node with an equivalent CPU implementation. We present detailed performance analyses, parallel scalability results, and accuracy assessments for GWs computed for mass ratios $\mathrm{q}=1,2,4$., We also present strong scalability up to 8 A100s and weak scaling up to 229,376 x86 cores on the Texas Advanced Computing Center's Frontera system.