Multi-level Customisation Framework for Curve Based Monte Carlo Financial Simulations

  • Qiwei Jin
  • Diwei Dong
  • Anson H. T. Tse
  • Gary C. T. Chow
  • David B. Thomas
  • Wayne Luk
  • Stephen Weston
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7199)

Abstract

One of the main challenges when accelerating financial applications using reconfigurable hardware is the management of design complexity. This paper proposes a multi-level customisation framework for automatic generation of complex yet highly efficient curve based financial Monte Carlo simulators on reconfigurable hardware. By identifying multiple levels of functional specialisations and the optimal data format for the Monte Carlo simulation, we allow different levels of programmability in our framework to retain good performance and support multiple applications. Designs targeting a Virtex-6 SX475T FPGA generated by our framework are about 40 times faster than single-core software implementations on an i7-870 quad-core CPU at 2.93 GHz; they are over 10 times faster and 20 times more energy efficient than 4-core implementations on the same i7-870 quad-core CPU, and are over three times more energy efficient and 36% faster than a highly optimised implementation on an NVIDIA Tesla C2070 GPU at 1.15 GHz. In addition, our framework is platform independent and can be extended to support CPU and GPU applications.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Boland, D., Constantinides, G.: Automated precision analysis: A polynomial algebraic approach. In: IEEE Int. Symp. on Field-Programmable Custom Computing Machines (FCCM) (2010)Google Scholar
  3. 3.
    de Dinechin, F., Pasca, B.: Floating-point exponential functions for DSP-enabled FPGAs. In: Proc. Int. Conf. on Field-Programmable Technology (2010)Google Scholar
  4. 4.
    Fang, C.F., Rutenbar, R.A., Chen, T.: Fast, accurate static analysis for fixed-point finite-precision effects in DSP designs. In: Proc. Int. Conf. on Computer-Aided Design (2003)Google Scholar
  5. 5.
    Fousse, L., Hanrot, G., Lefèvre, V., Pélissier, P., Zimmermann, P.: Mpfr: A multiple-precision binary floating-point library with correct rounding. ACM Trans. Math. Softw. 33 (2007)Google Scholar
  6. 6.
    Gaffar, A.A., Mencer, O., Luk, W., Cheung, P.Y.K.: Unifying bit-width optimisation for fixed-point and floating-point designs. In: IEEE Symp. on Field-Programmable Custom Computing Machines (2004)Google Scholar
  7. 7.
    Heath, D., Jarrow, R., Morton, A.: Bond pricing and the term structure of interest rates: A new methodology for contingent claims valuation. Econometrica 60(1), 77–105 (1992)CrossRefMATHGoogle Scholar
  8. 8.
    Ho, T.S.Y., Lee, S.B.: Term structure movements and pricing interest rate contingent claims. Journal of Finance 41(5), 1011–1029 (1986)CrossRefGoogle Scholar
  9. 9.
    Jin, Q., Thomas, D.B., Luk, W., Cope, B.: Exploring reconfigurable architectures for tree-based option pricing models. ACM Trans. Reconfigurable Technol. Syst. 2, 21:1–21:17 (2009)CrossRefGoogle Scholar
  10. 10.
    Kum, K.I., Sung, W.: Combined word-length optimization and high-level synthesis of digital signal processing systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 20, 921–930 (2001)CrossRefGoogle Scholar
  11. 11.
    Larsen, R.J., Marx, M.L.: An Introduction to Mathematical Statistics and Its Applications. Pearson Education (2011)Google Scholar
  12. 12.
    Lee, D.U., Gaffar, A., Cheung, R., Mencer, O., Luk, W., Constantinides, G.: Accuracy-guaranteed bit-width optimization. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 25, 1990–2000 (2006)CrossRefGoogle Scholar
  13. 13.
    Morris, G., Aubury, M.: Design space exploration of the European option benchmark using Hyperstreams. In: Proc. Int. Conf. on Field Programmable Logic and Applications, pp. 5–10 (2007)Google Scholar
  14. 14.
    Thomas, D.B., Luk, W.: High quality uniform random number generation using LUT optimised state-transition matrices. J. VLSI Signal Process. Syst. 47, 77–92 (2007)CrossRefGoogle Scholar
  15. 15.
    Thomas, D., Bower, J., Luk, W.: Automatic generation and optimisation of reconfigurable financial Monte-Carlo simulations. In: Proc. Int. Conf. on Application-Specific Systems, Architectures and Processors, pp. 685–689 (2007)Google Scholar
  16. 16.
    Thomas, D., Luk, W.: Non-uniform random number generation through piecewise linear approximations. In: Proc. Int. Conf. on Field Programmable Logic and Applications (2006)Google Scholar
  17. 17.
    Tian, X., Benkrid, K.: American option pricing on reconfigurable hardware using least-squares monte carlo method. In: Proc. Int. Conf. on Field-Programmable Technology, pp. 263 –270 (2009)Google Scholar
  18. 18.
    Tse, A.H., Thomas, D.B., Tsoi, K., Luk, W.: Reconfigurable control variate Monte-Carlo designs for pricing exotic options. In: Proc. Int. Conf. on Field Programmable Logic and Applications, pp. 364–367 (2010)Google Scholar
  19. 19.
    Weston, S., Spooner, J., Racanière, S., Mencer, O.: Rapid computation of value and risk for derivatives portfolios. Concurrency and Computation: Practice and Experience (to appear)Google Scholar
  20. 20.
    Zhang, G., Leong, P., Ho, C., Tsoi, K., Cheung, C., Lee, D.U., Cheung, R., Luk, W.: Reconfigurable acceleration for Monte-Carlo based financial simulation. In: Proc. IEEE Int. Conf. on Field-Programmable Technology, pp. 215–224 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Qiwei Jin
    • 1
  • Diwei Dong
    • 2
  • Anson H. T. Tse
    • 1
  • Gary C. T. Chow
    • 1
  • David B. Thomas
    • 3
  • Wayne Luk
    • 1
  • Stephen Weston
    • 4
  1. 1.Department of ComputingImperial College LondonUK
  2. 2.Department of MathematicsImperial College LondonUK
  3. 3.Department of Electrical and Electronic EngineeringImperial College LondonUK
  4. 4.Credit Quantitative Research, J.P. MorganLondonUK

Personalised recommendations