Polar generation of random variates with the t-distribution
Mathematics of Computation
Algorithm 659: Implementing Sobol's quasirandom sequence generator
ACM Transactions on Mathematical Software (TOMS)
Remark on algorithm 659: Implementing Sobol's quasirandom sequence generator
ACM Transactions on Mathematical Software (TOMS)
Parallel and Distributed Computing Issues in Pricing Financial Derivatives through Quasi Monte Carlo
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Variance Reduction Techniques for Estimating Value-at-Risk
Management Science
The Journal of Supercomputing
Low discrepancy sequences in high dimensions: How well are their projections distributed?
Journal of Computational and Applied Mathematics
Multivariate Gaussian Random Number Generation Targeting Reconfigurable Hardware
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
Pattern-oriented application frameworks for domain experts to effectively utilize highly parallel manycore microprocessors
Special Issue for the Workshop on High Performance Computational Finance
Concurrency and Computation: Practice & Experience
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Values of portfolios in modern financial markets may change precipitously with changing market conditions. The utility of financial risk management tools is dependent on whether they can estimate Value-at-Risk (VaR) of portfolios on-demand when key decisions need to be made. However, VaR estimation of portfolios uses the Monte Carlo method, which is a computationally intensive method often run as an overnight batch job. With the proliferation of highly parallel computing platforms such as multicore CPUs and manycore graphics processing units (GPUs), teraFLOPS of computation capability is now available on a desktop computer, enabling the VaR of large portfolios with thousands of risk factors to be computed within only a fraction of a second. Achieving such performance in practice requires the assimilation of expertise in the following three areas: (i) application domain; (ii) statistical analytics; and (iii) parallel computing. This paper demonstrates that these areas of expertise inform optimization perspectives that, when combined, lead to 127×speedup on our CPU-based implementation and 538×speedup on our GPU-based implementation. Copyright © 2011 John Wiley & Sons, Ltd.