Monte Carlo simulation is widely used to numerically solve stochastic differential equations. Although the method is flexible and easy to implement, it may be slow to converge. Moreover, an inaccurate solution will result when using large time steps. The Seven League scheme, a deep learning-based numerical method, has been proposed to address these issues. This paper generalizes the scheme regarding parallel computing, particularly on Graphics Processing Units (GPUs), improving the computational speed.

翻译：蒙特卡洛模拟被广泛用于随机微分方程的数值求解。尽管该方法灵活且易于实现，但其收敛速度较慢。此外，采用大步长计算时将导致解不精确。针对上述问题，基于深度学习的数值方法——七里格格式被提出。本文将该格式拓展至并行计算领域，特别是在图形处理器（GPU）上实现，从而提升了计算速度。