{"title":"GPU期权定价","authors":"Simon Suo, Ruiming Zhu, Ryan Attridge, J. Wan","doi":"10.1145/2830556.2830564","DOIUrl":null,"url":null,"abstract":"In this paper, we explore the possible approaches to harness extra computing power from commodity hardware to speedup pricing calculation of individual options. Specifically, we leverage two parallel computing platforms: Open Computing Language (OpenCL) and Compute United Device Architecture (CUDA). We propose several parallel implementations of the two most popular numerical methods of option pricing: Lattice model and Monte Carlo method. In the end, we show that the parallel implementations achieve significant performance improvement over serial implementations.","PeriodicalId":254831,"journal":{"name":"Proceedings of the 8th Workshop on High Performance Computational Finance","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"GPU option pricing\",\"authors\":\"Simon Suo, Ruiming Zhu, Ryan Attridge, J. Wan\",\"doi\":\"10.1145/2830556.2830564\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we explore the possible approaches to harness extra computing power from commodity hardware to speedup pricing calculation of individual options. Specifically, we leverage two parallel computing platforms: Open Computing Language (OpenCL) and Compute United Device Architecture (CUDA). We propose several parallel implementations of the two most popular numerical methods of option pricing: Lattice model and Monte Carlo method. In the end, we show that the parallel implementations achieve significant performance improvement over serial implementations.\",\"PeriodicalId\":254831,\"journal\":{\"name\":\"Proceedings of the 8th Workshop on High Performance Computational Finance\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 8th Workshop on High Performance Computational Finance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2830556.2830564\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 8th Workshop on High Performance Computational Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2830556.2830564","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we explore the possible approaches to harness extra computing power from commodity hardware to speedup pricing calculation of individual options. Specifically, we leverage two parallel computing platforms: Open Computing Language (OpenCL) and Compute United Device Architecture (CUDA). We propose several parallel implementations of the two most popular numerical methods of option pricing: Lattice model and Monte Carlo method. In the end, we show that the parallel implementations achieve significant performance improvement over serial implementations.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
