FPGA acceleration of quasi-Monte Carlo in finance
Nathan A. Woods, Tom VanCourt · 2008
Today, quasi-Monte Carlo (QMC) methods are widely used in finance to price derivative securities. The QMC approach is popular because for many types of derivatives it yields an estimate of the price, to a given accuracy, faster than other competitive approaches, like Monte Carlo (MC) methods. The calculation of the large number of underlying asset pathways consumes a significant portion of the overall run-time and energy of modern QMC derivative pricing simulations. Therefore, we present an FPGA-based accelerator for the calculation of asset pathways suitable for use in the QMC pricing of several types of derivative securities. Although this implementation uses constructs (recursive algorithms and double-precision floating point) not normally associated with successful FPGA computing, we demonstrate performance in excess of 50times that of a 3 GHz multi-core processor.