Stratified regression Monte-Carlo scheme for semilinear PDEs and BSDEs with large scale parallelization on GPUs

Gobet, Emmanuel; López-Salas, José Germán; Turkedjiev, Plamen; Vázquez, Carlos

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http://hdl.handle.net/2183/38128

Stratified regression Monte-Carlo scheme for semilinear PDEs and BSDEs with large scale parallelization on GPUs

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Gobet_Emmanuel_2016_Stratified_regression_Monte_Carlo_PDE_BSDE.pdf (692.73 KB)

Identifiers

URI: http://hdl.handle.net/2183/38128

Publication date

2016-06

Authors

Gobet, Emmanuel

López-Salas, José Germán

Turkedjiev, Plamen

Vázquez, Carlos

Bibliographic citation

E. 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: 10.1137/16M106371X

Abstract

[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.

Description

©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