請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/19225完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 呂育道 | |
| dc.contributor.author | Hsien-Cheng Chen | en |
| dc.contributor.author | 陳賢誠 | zh_TW |
| dc.date.accessioned | 2021-06-08T01:49:33Z | - |
| dc.date.copyright | 2016-08-03 | |
| dc.date.issued | 2016 | |
| dc.date.submitted | 2016-07-29 | |
| dc.identifier.citation | [1] Basic Linear Algebra Subprograms, Wikipedia, https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms
[2] Billeter, M., Olsson, O., Assarsson, U., “Efficient Stream Compaction on Wide SIMD Many-Core Architectures,” In Proceedings of the Conference on High Performance Graphics, New York, pp. 159–166, 2009. [3] Bischof, C.H., Van Loan, C., “The WY Representation for Products of Householder Matrices,” Department of Computer Science, Cornell University, 1985. [4] Ching-Wen Chen‚ Kuan-Lin Huang, Yuh-Dauh Lyuu, “Accelerating the Least-Square Monte Carlo Method with Parallel Computing,” Journal of Supercomputing, Vol. 71, No. 9, pp. 3593–3608, 2015. [5] CUDA, Wikipedia, https://zh.wikipedia.org/wiki/CUDA [6] Harris, M., Sengupta, S., Owens, J.D., “Parallel Prefix Sum (Scan) with CUDA,” GPUGems3 (Chapter 39), Boston: Addison-Wesley, 2007. [7] Hull, J.C., Options, Futures, and Other Derivatives, 8th Edition, Upper Saddle River, NJ: Prentice-Hall, 2011. [8] Intel Corporation, Intel Math Kernel Library Reference Manual, Santa Clara, CA: Intel, 2007. [9] Kerr, A., Campbell, D., Richards, M., “QR Decomposition on GPUs,” In Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units (GPGPU-2), New York, pp. 71–78, 2009. [10] Longstaff, F.A., Schwartz, E.S., “Valuing American Options by Simulation: A Simple Least-Squared Approach,” Review of Financial Studies, Vol.13, No.1, pp.113–147, 2001. [11] NVIDIA Corporation, CUDA Programming Guide, Version 7.5, Santa Clara, CA: NVIDIA, 2015. [12] NVIDIA Corporation, CUDA Toolkit Version 7.5 CUBLAS Library, Santa Clara, CA: NVIDIA, 2015. [13] QR Decomposition, Wikipedia, https://en.wikipedia.org/wiki/QR_decomposition [14] Spataro, D., Stream Compaction on GPU – Efficient Implementation – CUDA, http://www.davidespataro.it/cuda-stream-compaction-efficient-implementation/ [15] 周秉誼, GPU高效能運算環境—CUDA與GPU Cluster介紹, http://www.cc.ntu.edu.tw/chinese/epaper/0012/20100320_1205.htm [16] 周志成, Householder變換於 QR 分解的應用, 線代啟示錄, https://ccjou.wordpress.com/2011/05/24/householder-%E8%AE%8A%E6%8F%9B%E6%96%BC-qr-%E5%88%86%E8%A7%A3%E7%9A%84%E6%87%89%E7%94%A8/ [17] 最小平方法, http://baike.baidu.com/view/139822.htm?fromtitle=%E6%9C%80%E5%B0%8F%E5%B9%B3%E6%96%B9%E6%B3%95&fromid=7022192&type=syn | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/19225 | - |
| dc.description.abstract | 最小平方蒙地卡羅法是一種美式選擇權的評價方法。此方法通常計算量很大，需要花費許多運算時間，才能得出最終價格。在本篇論文中，我們以資料平行(data parallelism)的方式，將原本最小平方法蒙地卡羅法依路徑，分為許多互相獨立的組。在最小平方法的部分，我們採用QR分解進行求解。我們在GPU上使用CUDA針對美式賣權實作此平行方法，並且與在CPU上的循序版本做比較。
數值實驗的結果顯示當所分的組越多時，所花的執行時間就越少，但相對找出來的賣權價格也會越高估。 | zh_TW |
| dc.description.abstract | 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. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T01:49:33Z (GMT). No. of bitstreams: 1 ntu-105-R03922022-1.pdf: 1376468 bytes, checksum: aef49f35f1ae19b7669886d313231fbf (MD5) Previous issue date: 2016 | en |
| dc.description.tableofcontents | 誌謝 ii
摘要 iii Abstract iv 第一章 導論 1 1.1 簡介 1 1.2 論文架構 3 第二章 背景知識 4 2.1 選擇權簡介 4 2.2 最小平方蒙地卡羅法(Least-Squares Monte Carlo Method) 5 2.3 CUDA(Compute Unified Device Architecture)簡介 8 2.4 最小平分法–QR分解 12 2.5 Stream Compaction 16 2.6 BLAS數學函式庫工具 20 第三章 實驗設計 21 第四章 實驗結果分析 23 第五章 結論與展望 28 5.1 結論 28 5.2 未來展望 28 參考文獻 29 | |
| dc.language.iso | zh-TW | |
| dc.title | 使用圖型處理器加速最小平方蒙地卡羅法 | zh_TW |
| dc.title | Using GPU to Accelerate the Least-Squares Monte Carlo Method | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 104-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 戴天時,張經略 | |
| dc.subject.keyword | 最小平方蒙地卡羅法,資料平行,圖型處理器,統一計算架構, | zh_TW |
| dc.subject.keyword | Least-squares Monte Carlo,data parallelism,Graphic Processing Unit (GPU),Compute Unified Device Architecture (CUDA), | en |
| dc.relation.page | 31 | |
| dc.identifier.doi | 10.6342/NTU201600229 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2016-07-29 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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