CUDA implementation of barrier option valuation with jump-diffusion process and Brownian bridge

High Performance Computing on graphics processors (GPUs) has produced excellent results in a wide array of disciplines. Compute bound problems benefit from the massive parallelism and memory bound problems benefit from higher bandwidth and the ability to hide latency. In this work we apply GPU computing to a non-trivial option valuation problem to demonstrate its efficacy on problems with real world significance. Here we have focussed attention on barrier options modeled using an underlying jump-diffusion process and incorporating a Brownian bridge to account for inter-jump crossings. Exotic path-dependent options such as this often lack a closed-form solution and numerical methods must be used in their pricing. Monte Carlo methods which are commonly utilized involve simulation of the price trajectory along many independent paths, an approach that maps well to the GPU thread concept. Here we present the results of our CPU and GPU implementations comparing performance and providing details on both.