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

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.