research-article

Multivariate Gaussian Random Number Generation Targeting Reconfigurable Hardware

Authors:

David B. Thomas,

Wayne LukAuthors Info & Claims

ACM Transactions on Reconfigurable Technology and Systems (TRETS), Volume 1, Issue 2

Article No.: 12, Pages 1 - 29

https://doi.org/10.1145/1371579.1371584

Published: 01 June 2008 Publication History

Abstract

The multivariate Gaussian distribution is often used to model correlations between stochastic time-series, and can be used to explore the effect of these correlations across N time-series in Monte-Carlo simulations. However, generating random correlated vectors is an O(N²) process, and quickly becomes a computational bottleneck in software simulations. This article presents an efficient method for generating vectors in parallel hardware, using N parallel pipelined components to generate a new vector every N cycles. This method maps well to the embedded block RAMs and multipliers in contemporary FPGAs, particularly as extensive testing shows that the limited bit-width arithmetic does not reduce the statistical quality of the generated vectors. An implementation of the architecture in the Virtex-4 architecture achieves a 500MHz clock-rate, and can support vector lengths up to 512 in the largest devices. The combination of a high clock-rate and parallelism provides a significant performance advantage over conventional processors, with an xc4vsx55 device at 500MHz providing a 200 times speedup over an Opteron 2.6GHz using an AMD optimised BLAS package. In a case study in Delta-Gamma Value-at Risk, an RC2000 accelerator card using an xc4vsx55 at 400MHz is 26 times faster than a quad Opteron 2.6GHz SMP.

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Mandal ADutta SPramanik S(2021)Machine Intelligence of Pi From Geometrical Figures With Variable Parameters Using SCILabMethodologies and Applications of Computational Statistics for Machine Intelligence10.4018/978-1-7998-7701-1.ch003(38-63)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-7701-1.ch003
Elvira VMartino LClosas P(2021)Importance Gaussian QuadratureIEEE Transactions on Signal Processing10.1109/TSP.2020.304552669(474-488)Online publication date: 2021
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Index Terms

Multivariate Gaussian Random Number Generation Targeting Reconfigurable Hardware
1. Hardware
  1. Integrated circuits
    1. Logic circuits
      1. Arithmetic and datapath circuits

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cover image ACM Transactions on Reconfigurable Technology and Systems

ACM Transactions on Reconfigurable Technology and Systems Volume 1, Issue 2

June 2008

143 pages

ISSN:1936-7406

EISSN:1936-7414

DOI:10.1145/1371579

Issue’s Table of Contents

Copyright © 2008 ACM.

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Association for Computing Machinery

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Publication History

Published: 01 June 2008

Accepted: 01 March 2008

Revised: 01 February 2008

Received: 01 August 2007

Published in TRETS Volume 1, Issue 2

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Panjei EGruenwald L(2024)Discovering outlying attributes of outliers in data streamsData & Knowledge Engineering10.1016/j.datak.2024.102349(102349)Online publication date: Aug-2024
https://doi.org/10.1016/j.datak.2024.102349
Mandal ADutta SPramanik S(2021)Machine Intelligence of Pi From Geometrical Figures With Variable Parameters Using SCILabMethodologies and Applications of Computational Statistics for Machine Intelligence10.4018/978-1-7998-7701-1.ch003(38-63)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-7701-1.ch003
Elvira VMartino LClosas P(2021)Importance Gaussian QuadratureIEEE Transactions on Signal Processing10.1109/TSP.2020.304552669(474-488)Online publication date: 2021
https://doi.org/10.1109/TSP.2020.3045526
Cao YJiang SGuo SLing WZhou XYu Z(2021)Real-Time SAR Imaging Based on Reconfigurable ComputingIEEE Access10.1109/ACCESS.2021.30932999(93684-93690)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3093299
Sulewski P(2021)Two methods of conjoint summands of generating bivariate and trivariate normal pseudo-random numbersJournal of Statistical Computation and Simulation10.1080/00949655.2021.200559692:8(1714-1739)Online publication date: 27-Nov-2021
https://doi.org/10.1080/00949655.2021.2005596
Harris SChamberlain RGill C(2020)OpenCL Performance on the Intel Heterogeneous Architecture Research Platform2020 IEEE High Performance Extreme Computing Conference (HPEC)10.1109/HPEC43674.2020.9286213(1-9)Online publication date: 22-Sep-2020
https://doi.org/10.1109/HPEC43674.2020.9286213
Zhao X(2020)Enterprise financial investment platform simulation based on FPGA-based high-performance algorithmMicroprocessors and Microsystems10.1016/j.micpro.2020.103408(103408)Online publication date: Nov-2020
https://doi.org/10.1016/j.micpro.2020.103408
Li YZhang M(2019)A FPGA Based Parallel Multivariate Gaussian Random Number Generation Framework2019 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)10.1109/ICAICA.2019.8873482(126-129)Online publication date: Mar-2019
https://doi.org/10.1109/ICAICA.2019.8873482
Thomas DLuk W(2013)Multiplierless Algorithm for Multivariate Gaussian Random Number Generation in FPGAsIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2012.222801721:12(2193-2205)Online publication date: 1-Dec-2013
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Zhoubing Xiong Sottile FSpirito MGarello R(2013)Analysis of hybrid and cooperative positioning algorithms in urban canyon scenarios2013 International Conference on Localization and GNSS (ICL-GNSS)10.1109/ICL-GNSS.2013.6577257(1-6)Online publication date: Jun-2013
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