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

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2 Author(s)
Xiang Tian ; The University of Edinburgh, School of Engineering and Electronics, King¿s Buildings, Mayfield Road, EH9 3JL, Scotland, UK ; Khaled Benkrid

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.

Published in:

2008 Second International Workshop on High-Performance Reconfigurable Computing Technology and Applications

Date of Conference:

16-16 Nov. 2008