GPU acceleration for the pricing of the CMS spread option

This paper presents a study on the pricing of a financial derivative using parallel algorithms which are optimised to run on a GPU. Our chosen financial derivative, the constant maturity swap (CMS) spread option, has an associated pricing model which incorporates several algorithmic steps, including: evaluation of probability distributions, implied volatility root-finding, integration and copula simulation. The novel aspects of the analysis are: (1) a fast new accurate double precision normal distribution approximation for the GPU (based on the work of Ooura), (2) a parallel grid search algorithm for calculating implied volatility and (3) an optimised data and instruction workflow for the pricing of the CMS spread option. The study is focused on 91.5% of the runtime of a benchmark (CPU based) model and results in a speed-up factor of 10.3 when compared to our single-threaded benchmark model. Our work is implemented in double precision using the NVIDIA GF100 architecture.