Monte-Carlo simulation-based financial computing on the Maxwell FPGA parallel machine

Abstract

Efficient computational solutions for scientific and engineering problems are a priority for many governments around the world, as they can offer major economic comparative advantages. Financial computing problems are a prime example of such problems where even the slightest improvements in execution times and latency can generate large amounts of extra profits. However, financial computing has not benefited relatively greatly from early developments in high performance computing, as the latter aimed mainly at engineering and weapon design applications. Besides, financial experts were initially focusing on developing mathematical models and computer simulations in order to comprehend the behavior of financial markets and develop risk-management tools. As this effort progressed, the complexity of financial computing applications grew up rapidly. Hence, high performance computing turned out to be very important in the field of finance. Many financial models do not have a practical closed-form solution in which case numerical methods are the only alternative. Monte-Carlo simulation is one of the most commonly used numerical methods, in financial modeling and scientific computing in general, with huge computation benefits in solving problems where closed-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 harnessed to accelerate the computation. For this, high performance computers, increasingly with off-the-shelf accelerator hardware, are being proposed as an economic high performance implementation platform for Monte-Carlo-based simulations. Field programmable gate arrays (FPGAs) in particular have been recently proposed as a high performance and relatively low power acceleration platform for such applications.In light of the above, the project presented in this chapter develops novel FPGA hardware architectures for Monte-Carlo simulations of different types of financial option pricing models, namely European, Asian, and American options, the stochastic volatility model (GARCH model), and Quasi-Monte Carlo simulation. These architectures have been implemented on an FPGA-based supercomputer, called Maxwell, developed at the University of Edinburgh, which is one of the few openly available FPGA parallel machines in the world. Maxwell is a 32-CPU cluster augmented with 64 Virtex-4 Xilinx FPGAs connected in a 2D torus. Our hardware implementations all show significant computing efficiency compared to traditional software-based implementations, which in turn shows that reconfigurable computing technology can be an efficacious and efficient platform for high performance computing applications, particularly financial computing.