2014/08/23 by Linlin Xu, Xu, Linlin, Giray Ökten +2
Economics, Econometrics and Finance · Mathematics · #Computational Finance (q-fin.CP) #FOS: Economics and business #Markov Chains and Monte Carlo Methods #Mathematical Approximation and Integration #Monetary Policy and Economic Impact #Stochastic processes and financial applications #q-fin.CP
paper · pdf · doi:10.48550/arxiv.1408.5526
arxiv created 2014/08/23 · openalex publication_date 2014/08/23 · arxiv updated 2014/08/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
GPU computing has become popular in computational finance and many financial institutions are moving their CPU based applications to the GPU platform. Since most Monte Carlo algorithms are embarrassingly parallel, they benefit greatly from parallel implementations, and consequently Monte Carlo has become a focal point in GPU computing. GPU speed-up examples reported in the literature often involve Monte Carlo algorithms, and there are software tools commercially available that help migrate Monte Carlo financial pricing models to GPU. We present a survey of Monte Carlo and randomized quasi-Monte Carlo methods, and discuss existing (quasi) Monte Carlo sequences in GPU libraries. We discuss specific features of GPU architecture relevant for developing efficient (quasi) Monte Carlo methods. We introduce a recent randomized quasi-Monte Carlo method, and compare it with some of the existing implementations on GPU, when they are used in pricing caplets in the LIBOR market model and mortgage backed securities.