  In the smooth mass distribution model, the critical curve represents a line
with magnification divergence on the image plane in a strong gravitational
lensing system. Considering the microlensing effects caused by discrete masses,
the magnification map in the source plane exhibits a complex structure, which
offers a promising way for detecting dark matter. However, simulating
microlensing near the critical curve poses challenges due to magnification
divergence and the substantial computational demands involved. To achieve the
required simulation accuracy, direct inverse ray-shooting would require
significant computational resources. Therefore we applied a GPU-based code
optimized with interpolation method to enable efficient computation on a large
scale. Using the GPU of NVIDIA Tesla V100S PCIe 32GB, it takes approximately
7000 seconds to calculate the effects of around 13,000 microlenses for a
simulation involving 1013 emitted rays. Then we generated 80 magnification
maps, and select 800 light curves for a statistical analysis of microcaustic
density and peak magnification.
