Stratified regression Monte-Carlo scheme for semilinear PDEs and BSDEs with large scale parallelization on GPUs - École polytechnique Access content directly
Journal Articles SIAM Journal on Scientific Computing Year : 2016

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

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.
Fichier principal
Vignette du fichier
gltv-sisc.pdf (596.13 Ko) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-01186000 , version 1 (27-08-2015)

Identifiers

Cite

Emmanuel Gobet, Jose Lopez-Salas, Plamen Turkedjiev, C. Vázquez. Stratified regression Monte-Carlo scheme for semilinear PDEs and BSDEs with large scale parallelization on GPUs. SIAM Journal on Scientific Computing, 2016, 38 (6), pp.C652-C677. ⟨10.1137/16M106371X⟩. ⟨hal-01186000⟩
575 View
737 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More