Financial option pricing on APU

Abstract

Financial option pricing is a compute-intensive problem that requires real-time pricing for making decisions on investment portfolios or studying the risk value of a company's assets. In this study, we report our experiences designing an algorithm for a complex option pricing problem on the Accelerated Processing Unit (APU), a state-of-the-art multi-core architecture. Using a naive algorithm, both the APU and GPU do not perform well as there is a non-optimal use of memory which limits our utilization of computational resources. To improve performance we examined two methods of optimization: (i) vectorization of the computational domain and (ii) loop unrolling of the computation. Through these two methods we achieve better performance and scalability with less powerful hardware than other GPU solutions currently available. © 2012 Springer-Verlag.