用GPU加速星链级轨道预测，速度提升1500倍。
jaxsgp4: GPU-accelerated mega-constellation propagation with batch parallelism
- 基于JAX重写SGP4算法，实现批量并行计算。
- 单张A100 GPU可在4毫秒内完成星链全量卫星1000步预测。
- 32位精度兼顾速度与误差，适合大规模太空态势感知。
随着人类在轨物体从稀疏集群演变为超10万颗卫星的巨型星座，传统基于CPU的简化通用摄动4（SGP4）轨道预报方法面临严重瓶颈。本文提出开源高性能重实现jxsgp4，利用JAX库的即时编译、自动向量化和硬件优化能力，在纯函数式范式下实现现代GPU上的大规模并行传播。实验表明，jxsgp4可在单张A100 GPU上于4毫秒内完成9,341颗星链卫星的1,000步未来状态预测，相较传统C++基线提速1500倍。此外，研究论证32位浮点精度在SGP4任务中可实现精度损失极小但吞吐量显著提升的合理权衡，适用于硬件加速器场景。
原文摘要 · Abstract (English)
As the population of anthropogenic space objects transitions from sparse clusters to mega-constellations exceeding 100,000 satellites, traditional orbital propagation techniques face a critical bottleneck. Standard CPU-bound implementations of the Simplified General Perturbations 4 (SGP4) algorithm are less well suited to handle the requisite scale of collision avoidance and Space Situational Awareness (SSA) tasks. This paper introduces \texttt{jaxsgp4}, an open-source high-performance reimplementation of SGP4 utilising the \texttt{JAX} library. \texttt{JAX} has gained traction in the landscape of computational research, offering an easy mechanism for Just-In-Time (JIT) compilation, automatic vectorisation and automatic optimisation of code for CPU, GPU and TPU hardware modalities. By refactoring the algorithm into a pure functional paradigm, we leverage these transformations to execute massively parallel propagations on modern GPUs. We demonstrate that \texttt{jaxsgp4} can propagate the entire Starlink constellation (9,341 satellites) each to 1,000 future time steps in under 4 ms on a single A100 GPU, representing a speedup of $1500\times$ over traditional C++ baselines. Furthermore, we argue that the use of 32-bit precision for SGP4 propagation tasks offers a principled trade-off, sacrificing negligible precision loss for a substantial gain in throughput on hardware accelerators.
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