AI 中文总结
针对传统分波分析（PWA）框架在实验统计量增长时内存与速度不足的问题，提出高性能CTPWA框架，采用预计算、缓存及GPU似然计算等优化，速度较同类程序提升两个数量级，可用于BESIII等实验的高统计量PWA。
AI 中文摘要
分波分析（PWA）是强子物理中提取强子共振态性质的关键方法。随着实验统计量增长，传统实现方式在内存占用和速度方面面临严重困难。我们提出基于协变张量形式的高性能分波分析框架CTPWA，该框架采用大量预计算与缓存机制，以及完全基于GPU的似然计算与最小化。通过这些优化，拟合速度相较于基于autograd的GPU分波分析程序提升两个数量级，使BESIII等高精度实验中的高统计量分波分析成为可行。
英文摘要
Partial wave analysis (PWA) is a key method in hadron physics for extracting the properties of hadronic resonances. As experimental statistics grow, conventional implementations face severe difficulties in both memory usage and speed. We present \texttt{CTPWA}, a high performance PWA framework based on covariant tensor formalism. This framework adopts extensive precomputation and caching mechanisms, as well as fully GPU-based likelihood computation and minimization. With these optimization, the fitting speed is accelerated by two orders of magnitude relative to autograd-based GPU PWA programs, rendering high-statistics partial-wave analysis feasible at high-precision experiments like BESIII.
