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

Emmanuel Gobet 1 Jose Lopez-Salas 2 Plamen Turkedjiev 1 Carlos Vasquez 2
1 CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique
2 Department of Mathematics, Faculty of Informatics, Universidade da Coruna
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 : parallel computing em-pirical regressions Backward stochastic differential equations dynamic programming equation GPUs CUDA
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https://hal-polytechnique.archives-ouvertes.fr/hal-01186000
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Submitted on : Thursday, August 27, 2015 - 11:21:12 AM
Last modification on : Tuesday, December 8, 2020 - 9:48:06 AM
Long-term archiving on: : Saturday, November 28, 2015 - 10:15:01 AM

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Emmanuel Gobet, Jose Lopez-Salas, Plamen Turkedjiev, Carlos Vasquez. Stratified regression Monte-Carlo scheme for semilinear PDEs and BSDEs with large scale parallelization on GPUs. 2015. ⟨hal-01186000⟩

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