High Performance Monte-Carlo Based Option Pricing on FPGAs

High performance computing is becoming increasingly important in the field of financial computing, as the complexity of financial models continues to increase. Many of these financial models do not have a practical close form solution in which case numerical methods are the only alternative. Monte-Carlo simulation is one of most commonly used numerical methods, in scientific computing in general, with huge computation benefits in solving problems where close form solutions are impossible to derive. As the Monte-Carlo method relies on the average result of thousands of independent stochastic paths, massive parallelism can be adopted to accelerate the computation. Computer clusters with off-the-shelf accelerator hardware are increasingly being proposed as an economic high performance implementation platform for many scientific computing applications. This paper is part of this trend as it presents an implementation of a Monte-Carlo simulation engine for option pricing on an FPGA-based supercomputer, called Maxwell, developed at the University of Edinburgh. The latter consists of a 32 CPU cluster augmented with 64 Virtex-4 Xilinx FPGAs connected in a 2D torus. Our engine can implement various Monte-Carlo simulations on the Maxwell machine with speed-ups in excess of 100x compared to equivalent software implementations. This is illustrated in this paper in the context of an implementation of the GARCH option pricing model. Real hardware implementation shows that our FPGA-based implementation of the GARCH model outperforms an equivalent software implementation running on a workstation cluster with the same number of computing nodes (CPU/FPGA) by a factor of 340.