Parallelization and Acceleration of Dynamic Option Pricing Models on GPU-CPU Heterogeneous Systems

Brian Wesley MUGANDA, Bernard Shibwabo KASAMANI
{"title":"Parallelization and Acceleration of Dynamic Option Pricing Models on GPU-CPU Heterogeneous Systems","authors":"Brian Wesley MUGANDA, Bernard Shibwabo KASAMANI","doi":"10.21078/jssi-2023-0007","DOIUrl":null,"url":null,"abstract":"<p id=\"C1\">In this paper, stochastic global optimization algorithms, specifically, genetic algorithm and simulated annealing are used for the problem of calibrating the dynamic option pricing model under stochastic volatility to market prices by adopting a hybrid programming approach. The performance of this dynamic option pricing model under the obtained optimal parameters is also discussed. To enhance the model throughput and reduce latency, a heterogeneous hybrid programming approach on GPU was adopted which emphasized a data-parallel implementation of the dynamic option pricing model on a GPU-based system. Kernel offloading to the GPU of the compute-intensive segments of the pricing algorithms was done in OpenCL. The GPU approach was found to significantly reduce latency by an optimum of 541 times faster than a parallel implementation approach on the CPU, reducing the computation time from 46.24 minutes to 5.12 seconds.","PeriodicalId":258223,"journal":{"name":"Journal of Systems Science and Information","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Systems Science and Information","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21078/jssi-2023-0007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In this paper, stochastic global optimization algorithms, specifically, genetic algorithm and simulated annealing are used for the problem of calibrating the dynamic option pricing model under stochastic volatility to market prices by adopting a hybrid programming approach. The performance of this dynamic option pricing model under the obtained optimal parameters is also discussed. To enhance the model throughput and reduce latency, a heterogeneous hybrid programming approach on GPU was adopted which emphasized a data-parallel implementation of the dynamic option pricing model on a GPU-based system. Kernel offloading to the GPU of the compute-intensive segments of the pricing algorithms was done in OpenCL. The GPU approach was found to significantly reduce latency by an optimum of 541 times faster than a parallel implementation approach on the CPU, reducing the computation time from 46.24 minutes to 5.12 seconds.

GPU-CPU异构系统下动态期权定价模型的并行化与加速
本文采用随机全局优化算法,即遗传算法和模拟退火算法,采用混合规划方法对随机波动下的动态期权定价模型进行校正。讨论了该动态期权定价模型在得到的最优参数下的性能。为了提高模型的吞吐量和降低延迟,采用了一种基于GPU的异构混合编程方法,强调在基于GPU的系统上实现动态期权定价模型的数据并行化。定价算法中计算密集型部分的内核卸载到GPU是在OpenCL中完成的。研究发现,GPU方法显著降低了延迟,比CPU上的并行实现方法快541倍,将计算时间从46.24分钟减少到5.12秒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信