A Hardware Generator of Multi-point Distributed Random Numbers for Monte Carlo Simulation
Massimo Piccardi and
Eckhard Platen ()
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Filippo Martini: Faculty of Information Technology, University of Technology Sydney
Massimo Piccardi: Faculty of Information Technology, University of Technology Sydney
No 156, Research Paper Series from Quantitative Finance Research Centre, University of Technology, Sydney
Monte Carlo simulation of weak approximations of stochastic differential equations constitutes an intensive computational task. In applications such as finance, for instance, to achieve "real time" execution, as often required, one needs highly efficient implementations of the multi-point distributed random number generator underlying the simulations. In this paper a fast and flexible dedicated hardware solution on a field programmable gate array is presented. A comparative performance analysis between a software-only and the proposed hardware solution demonstrates that the hardware solution is bottleneck-free, retains the flexibility of the software solution and significantly increases the computational efficiency. Moreover, simulations in applications such as economics, insurance, physics, population dynamics, epidemiology, structural mechanics, chemistry and biotechnology can benefit from the obtained speedup.
Keywords: random number generators; random bit generators; hardware implementation; field programmable gate arrays (FPGAs); Monte Carlo simulation; weak Taylor schemes; multi-point distributed random variables (search for similar items in EconPapers)
JEL-codes: G10 G13 (search for similar items in EconPapers)
Pages: 20 pages
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-ets and nep-fin
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Published as: Bruti-Liberati, N., Nartini, F., Piccardi, M. and Platen, E., 2008, "A Hardware Generator of Multi-point Distributed Random Numbers for Monte Carlo Simulation", Mathematics and Computers in Simulation, 77(1), 45-56.
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Journal Article: A hardware generator of multi-point distributed random numbers for Monte Carlo simulation (2008)
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