Stratified regression Monte-Carlo scheme for semilinear PDEs and BSDEs with large scale parallelization on GPUs
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Stratified regression Monte-Carlo scheme for semilinear PDEs and BSDEs with large scale parallelization on GPUsDate
2016-06Citation
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.
Keywords
Backward stochastic differential equations
Dynamic programming equation
Em-pirical regressions
Parallel computing
GPUs
CUDA
Dynamic programming equation
Em-pirical regressions
Parallel computing
GPUs
CUDA
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
Editor version
ISSN
1064-8275
1095-7197
1095-7197