Massively parallelized Quasi-Monte Carlo financial simulation on a FPGA supercomputer

Abstract

Quasi-Monte Carlo simulation is a specialized Monte Carlo method which uses quasi-random, or low-discrepancy, numbers as the stochastic parameters. In many applications, this method has proved advantageous compared to the traditional Monte Carlo simulation method, which uses pseudo-random numbers, as it converges relatively quickly, and with a better level of accuracy. We implemented a massively parallelized Quasi-Monte Carlo simulation engine on a FPGA-based supercomputer, called Maxwell, and developed at the University of Edinburgh. Maxwell consists of 32 IBM Intel Xeon blades each hosting two Virtex-4 FPGA nodes through PCI-X interface. Real hardware implementation of our FPGA-based quasi-Monte Carlo engine on the Maxwell machine outperforms equivalent software implementations running on the Xeon processors by 3 orders of magnitude, with the speed-up figure scaling linearly with the number of processing nodes. The paper presents the detailed design and implementation of our Quasi-Monte Carlo engine in the context of financial derivatives pricing.