Improved Parallel Randomized Quasi Monte Carlo Algorithm of Asian Option Pricing on MIC Architecture
High-dimensional Option pricing,which plays an important role in complex financial activities,presents a great computational challenge in practice.Randomized Quasi Monte Carlo (RQMC) algorithm is of practical significance for forecasting option prices or other finance derivatives.In this paper,we present an improved parallel RQMC algorithm to forecast Asian option prices using Many Integrated Core (MIC) architecture.The improved algorithm employs novel data structure,independent random generator,vectorization technology,and data alignment.Numerical experiments were conducted on MIC architecture and the parallel performance was then analyzed.A speedup of 1.37 was achieved on MIC over CPU.Efficiency of 70.85%was achieved by using 64 OpenMP threads of a MIC card.An average speedup of 3.38 can be obtained by mixing the CPU and MIC computation in comparison with a single core of the CPU.Ample evidences proved the RQMC algorithm can benefit enormously from MIC architecture.
Quasi Monte Carlo Algorithm Asian Option Pricing MIC Architecture Parallel Simulation
Peng Hui Yao Yong Hong Hu Zhong Hua Lu Yan Gang Wang Jue Wang
Super-computing Center,Computer Network Information Center,Chinese Academy of Sciences;University of School of Statistics and Mathematics,Central University of Finance and Economics Super-computing Center,Computer Network Information Center,Chinese Academy of Sciences
国际会议
湖北咸宁
英文
157-161
2014-11-24（万方平台首次上网日期，不代表论文的发表时间）