IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v77y2008i1p45-56.html
   My bibliography  Save this article

A hardware generator of multi-point distributed random numbers for Monte Carlo simulation

Author

Listed:
  • Bruti-Liberati, Nicola
  • Martini, Filippo
  • Piccardi, Massimo
  • Platen, Eckhard

Abstract

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 speedups.

Suggested Citation

  • Bruti-Liberati, Nicola & Martini, Filippo & Piccardi, Massimo & Platen, Eckhard, 2008. "A hardware generator of multi-point distributed random numbers for Monte Carlo simulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 77(1), pages 45-56.
  • Handle: RePEc:eee:matcom:v:77:y:2008:i:1:p:45-56
    DOI: 10.1016/j.matcom.2007.01.031
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475407000158
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Nicola Bruti Liberati & Eckhard Platen, 2004. "On the Efficiency of Simplified Weak Taylor Schemes for Monte Carlo Simulation in Finance," Research Paper Series 114, Quantitative Finance Research Centre, University of Technology, Sydney.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tisan, A. & Cirstea, M., 2013. "SOM neural network design – A new Simulink library based approach targeting FPGA implementation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 91(C), pages 134-149.
    2. Sergio Chavez & Eckhard Platen, 2008. "Distributional Deviations in Random Number Generation in Finance," Research Paper Series 228, Quantitative Finance Research Centre, University of Technology, Sydney.
    3. Nicola Bruti-Liberati, 2007. "Numerical Solution of Stochastic Differential Equations with Jumps in Finance," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1.

    More about this item

    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;

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:matcom:v:77:y:2008:i:1:p:45-56. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.