In this paper, we design a novel algorithm based on Least-Squares Monte Carlo (LSMC) in order to approximate the solution of discrete time Backward Stochastic Differential Equations (BSDEs). Our algorithm allows massive parallelization of the computations on many core processors such as graphics processing units (GPUs). Our approach consists of a novel method of stratification which appears to be crucial for large scale parallelization. In this way, we minimize the exposure to the memory requirements due to the storage of simulations. Indeed, we note the lower memory overhead of the method compared with previous works.

翻译：本文基于最小二乘蒙特卡洛方法设计了一种新型算法，用于逼近离散时间倒向随机微分方程的解。该算法支持在图形处理器等多核处理器上实现大规模并行计算。本方法的核心在于提出了一种全新的分层策略，该策略对于大规模并行化至关重要。通过这种方式，我们显著降低了对模拟数据存储的内存需求。与已有研究相比，本方法确实表现出更低的内存开销。