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

作　　者：Brian Wesley MUGANDA Bernard Shibwabo KASAMANI 

机构地区：[1]Institute of Mathematical Sciences,Strathmore University,Ole Sangale Rd.,Nairobi 00200,Kenya [2]School of Computing and Engineering Sciences,Strathmore University,Ole Sangale Rd.,Nairobi 00200,Kenya

出　　处：《Journal of Systems Science and Information》2023年第5期622-635,共14页系统科学与信息学报（英文）

摘　　要：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.

关 键 词：PARALLELIZATION GPU computing option pricing GPU acceleration stochastic volatility hybrid programming 

分 类 号：TP332[自动化与计算机技术—计算机系统结构] TP18[自动化与计算机技术—计算机科学与技术]