Using GPU to Accelerate the Least-Squares Monte Carlo Method

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 104 === Least-squares Monte Carlo method (LSM) is a method for pricing American options. The approach can give accurate option prices but it is computation intensive. In this thesis we use data–parallelism techniques to accelerate LSM with GPUs; that is, we will divide...

Full description

Bibliographic Details
Main Authors: Hsien-Cheng Chen, 陳賢誠
Other Authors: 呂育道
Format: Others
Language:zh-TW
Published: 2016
Online Access:http://ndltd.ncl.edu.tw/handle/89493919774266460624
id ndltd-TW-104NTU05392119
record_format oai_dc
spelling ndltd-TW-104NTU053921192017-04-29T04:31:56Z http://ndltd.ncl.edu.tw/handle/89493919774266460624 Using GPU to Accelerate the Least-Squares Monte Carlo Method 使用圖型處理器加速最小平方蒙地卡羅法 Hsien-Cheng Chen 陳賢誠 碩士 國立臺灣大學 資訊工程學研究所 104 Least-squares Monte Carlo method (LSM) is a method for pricing American options. The approach can give accurate option prices but it is computation intensive. In this thesis we use data–parallelism techniques to accelerate LSM with GPUs; that is, we will divide the computation paths into mutually independent groups. As for the least-squares calculation, QR decomposition is employed. The program is implemented by using CUDA to run on GPUs. The numerical results are compared with a sequential program’s on CPUs. The experiment results show that the more groups are created, the less time it takes to execute. 呂育道 2016 學位論文 ; thesis 31 zh-TW
collection NDLTD
language zh-TW
format Others
sources NDLTD
description 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 104 === Least-squares Monte Carlo method (LSM) is a method for pricing American options. The approach can give accurate option prices but it is computation intensive. In this thesis we use data–parallelism techniques to accelerate LSM with GPUs; that is, we will divide the computation paths into mutually independent groups. As for the least-squares calculation, QR decomposition is employed. The program is implemented by using CUDA to run on GPUs. The numerical results are compared with a sequential program’s on CPUs. The experiment results show that the more groups are created, the less time it takes to execute.
author2 呂育道
author_facet 呂育道
Hsien-Cheng Chen
陳賢誠
author Hsien-Cheng Chen
陳賢誠
spellingShingle Hsien-Cheng Chen
陳賢誠
Using GPU to Accelerate the Least-Squares Monte Carlo Method
author_sort Hsien-Cheng Chen
title Using GPU to Accelerate the Least-Squares Monte Carlo Method
title_short Using GPU to Accelerate the Least-Squares Monte Carlo Method
title_full Using GPU to Accelerate the Least-Squares Monte Carlo Method
title_fullStr Using GPU to Accelerate the Least-Squares Monte Carlo Method
title_full_unstemmed Using GPU to Accelerate the Least-Squares Monte Carlo Method
title_sort using gpu to accelerate the least-squares monte carlo method
publishDate 2016
url http://ndltd.ncl.edu.tw/handle/89493919774266460624
work_keys_str_mv AT hsienchengchen usinggputoacceleratetheleastsquaresmontecarlomethod
AT chénxiánchéng usinggputoacceleratetheleastsquaresmontecarlomethod
AT hsienchengchen shǐyòngtúxíngchùlǐqìjiāsùzuìxiǎopíngfāngméngdekǎluófǎ
AT chénxiánchéng shǐyòngtúxíngchùlǐqìjiāsùzuìxiǎopíngfāngméngdekǎluófǎ
_version_ 1718445701000593408