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)


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.


Interest Rate Graphic Processing Unit FPGA Implementation Target Platform Graphic Processing Unit Implementation 
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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

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