FPGA-optimised high-quality uniform random number generators
Proceedings of the 16th international ACM/SIGDA symposium on Field programmable gate arrays
Multivariate Gaussian Random Number Generation Targeting Reconfigurable Hardware
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
Multivariate Gaussian Random Number Generator Targeting Specific Resource Utilization in an FPGA
ARC '08 Proceedings of the 4th international workshop on Reconfigurable Computing: Architectures, Tools and Applications
Word-Length Optimization and Error Analysis of a Multivariate Gaussian Random Number Generator
ARC '09 Proceedings of the 5th International Workshop on Reconfigurable Computing: Architectures, Tools and Applications
Efficient reconfigurable design for pricing asian options
ACM SIGARCH Computer Architecture News
FPGA Acceleration of MultiFactor CDO Pricing
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
| Hi-index | 0.00 |
The multivariate Gaussian distribution models random processes as vectors of Gaussian samples with a fixed correlation matrix. Such distributions are useful for modelling real-world multivariate time-series such as equity returns, where the returns for businesses in the same sector are likely to be correlated. Generating random samples from such a distribution presents a computational challenge due to the dense matrix-vector multiplication needed to introduce the required correlations. This paper proposes a hardware architecture for generating random vectors, utilising the embedded block RAMs and multipliers found in contemporary FPGAs. The approach generates a new n dimensional random vector every n clock cycles, and has a raw generation rate over 200 times that of a single Opteron 2.2GHz using an optimised BLAS package for linear algebra computation. The generation architecture is an ideal source for both software simulations connected via high bandwidth connection, and for completely FPGA based simulations. Practical performance is explored in a case study in Delta-Gamma Value-at-Risk, where a standalone Virtex-4 xc4vsx55 solution at 400 MHz is 33 times faster than a quad Opteron 2.2GHz SMP. The FPGA solution also scales well for larger problem sizes, allowing larger portfolios to be simulation.