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Accelerating numerical relativity with code generation: CUDA-enabled hyperbolic relaxation
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DOI：10.1088/1361-6382/add63e.png)
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Abstract
En 中文
Next-generation gravitational wave (GW) detectors such as Cosmic Explorer, the Einstein Telescope, and LISA, demand highly accurate and extensive GW catalogs to faithfully extract physical parameters from observed signals. However, numerical relativity (NR) faces significant challenges in generating these catalogs at the required scale and accuracy on modern computers, as NR codes do not fully exploit modern GPU capabilities. In response, we extend NRPy, a Python-based NR code-generation framework, to develop NRPyEllipticGPU-a CUDA-optimized elliptic solver tailored for the binary black hole initial data problem. NRPyEllipticGPU is the first GPU-enabled elliptic solver in the NR community, supporting a variety of coordinate systems and demonstrating substantial performance improvements on both consumer-grade and HPC-grade GPUs. We show that, when compared to a high-end CPU, NRPyEllipticGPU achieves on a high-end GPU up to a sixteenfold speedup in single precision while increasing double-precision performance by a factor of 2-4. This performance boost leverages the GPU's superior parallelism and memory bandwidth to achieve a compute-bound application and enhancing the overall simulation efficiency. As NRPyEllipticGPU shares the core infrastructure common to NR codes, this work serves as a practical guide for developing full, CUDA-optimized NR codes.
Keywords:
numerical relativity
binary black hole
initial data
hyperbolic relaxation
code generation
GPU acceleration