Gobet, EmmanuelLópez-Salas, José GermánTurkedjiev, PlamenVázquez, Carlos2024-07-182024-07-182016-06E. Gobet, J. G. López-Salas, P. Turkedjiev, y C. Vázquez, «Stratified Regression Monte-Carlo Scheme for Semilinear PDEs and BSDEs with Large Scale Parallelization on GPUs», SIAM J. Sci. Comput., vol. 38, n.o 6, pp. C652-C677, ene. 2016, doi: 10.1137/16M106371X1064-82751095-7197http://hdl.handle.net/2183/38128©2016 This version of the article has been accepted for publication, after peer review, but is not the version of record and does not reflect postacceptance improvements, or any corrections. The version of record is available online at: https://doi.org/10.1137/16M106371X[Abstract]: 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 multicore devices such as graphics processing units (GPUs). Our approach consists of a novel method of stratification which appears to be crucial for large scale parallelization.engBackward stochastic differential equationsDynamic programming equationEm-pirical regressionsParallel computingGPUsCUDAjournal articleopen access