Kavli Affiliate: Lile Wang
| First 5 Authors: Suwei Wang, Lile Wang, Subo Dong, ,
| Summary:
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.
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