Twinkle：一款基于GPU、采用轮廓积分法的双透镜微引力透镜代码
Twinkle: A GPU-based binary-lens microlensing code with contour integration method
AI总结：
本文介绍针对GPU等异构设备优化的双透镜微引力透镜建模代码Twinkle，通过改进透镜方程系数和鬼像识别提升稳定性，并实现约百倍加速。
摘要：
随着微引力透镜行星探测速率的快速提升，微引力透镜建模软件在计算效率方面面临重大挑战。为此，我们开发了Twinkle代码，这是一套高效且稳健的双透镜建模软件套件，针对异构计算设备（尤其是GPU）进行了优化。现有微引力透镜代码存在灾难性抵消问题，会削弱数值稳定性和精度，而Twinkle通过改进双透镜方程的系数解决了这些问题。我们还设计了一种改进方法，用于稳健地识别鬼像，从而提高计算可靠性。我们推进了现有技术水平：通过考虑GPU独特的任务和高速缓存调度模式，专门针对异构计算设备优化Twinkle，同时仍保持与CPU传统计算架构的兼容性。Twinkle已在当代GPU上展示出约2个数量级（约100倍以上）的加速。Twinkle计算速度的提升将转化为正在进行和即将开展的微引力透镜项目所需的准确、高效数据分析。Twinkle的GPU和CPU版本均为开源并公开发布。
英文摘要：
With the rapidly increasing rate of microlensing planet detections, microlensing modeling software faces significant challenges in computation efficiency. Here, we develop the Twinkle code, an efficient and robust binary-lens modeling software suite optimized for heterogeneous computing devices, especially GPUs. Existing microlensing codes have the issue of catastrophic cancellation that undermines the numerical stability and precision, and Twinkle resolves them by refining the coefficients of the binary-lens equation. We also devise an improved method for robustly identifying ghost images, thereby enhancing computational reliability. We have advanced the state of the art by optimizing Twinkle specifically for heterogeneous computing devices by taking into account the unique task and cache memory dispatching patterns of GPUs, while the compatibility with the traditional computing architectures of CPUs is still maintained. Twinkle has demonstrated an acceleration of approximately 2 orders of magnitude (>~100 times) on contemporary GPUs. The enhancement in computational speed of Twinkle will translate to the delivery of accurate and highly efficient data analysis for ongoing and upcoming microlensing projects. Both GPU and CPU versions of Twinkle are open-source and publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
