High-Performance Quasi-Monte Carlo Financial Simulation: FPGA vs. GPP vs. GPU

  • Authors:

  • Xiang Tian;Khaled Benkrid

  • Affiliations:

  • The University of Edinburgh;The University of Edinburgh

  • Venue:
  • ACM Transactions on Reconfigurable Technology and Systems (TRETS)
  • Year:
  • 2010

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Abstract

Quasi-Monte Carlo simulation is a special Monte Carlo simulation method that uses quasi-random or low-discrepancy numbers as random sample sets. In many applications, this method has proved advantageous compared to the traditional Monte Carlo simulation method, which uses pseudo-random numbers, thanks to its faster convergence and higher level of accuracy. This article presents the design and implementation of a massively parallelized Quasi-Monte Carlo simulation engine on an FPGA-based supercomputer, called Maxwell. It also compares this implementation with equivalent graphics processing units (GPUs) and general purpose processors (GPP)-based implementations. The detailed comparison between these three implementations (FPGA vs. GPP vs. GPU) is done in the context of financial derivatives pricing based on our Quasi-Monte Carlo simulation engine. Real hardware implementations on the Maxwell machine show that FPGAs outperform equivalent GPP-based software implementations by 2 orders of magnitude, with the speed-up figure scaling linearly with the number of processing nodes used (FPGAs/GPPs). The same implementations show that FPGAs achieve a ~ 3x speedup compared to equivalent GPU-based implementations. Power consumption measurements also show FPGAs to be 336x more energy efficient than CPUs, and 16x more energy efficient than GPUs.