Numerical integration of the three-dimensional Green kernel for an electromagnetic problem
Journal of Computational Physics
Word-length optimization for differentiable nonlinear systems
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Neural Network Implementation Using CUDA and OpenMP
DICTA '08 Proceedings of the 2008 Digital Image Computing: Techniques and Applications
Exploring Reconfigurable Architectures for Tree-Based Option Pricing Models
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
Accelerating Quadrature Methods for Option Valuation
FCCM '09 Proceedings of the 2009 17th IEEE Symposium on Field Programmable Custom Computing Machines
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This paper presents a novel parallel architecture for accelerating quadrature methods used for pricing complex multi-dimensional options, such as discrete barrier, Bermudan and American options. We explore different designs of the quadrature evaluation core including optimized pipelined hardware designs in reconfigurable logic and a compute unified device architecture (CUDA)-based graphics processing unit (GPU) design. A parametrizable automated system is presented for generating hardware quadrature evaluation cores with an arbitrary number of dimensions. The performance and energy consumption of field-programmable gate arrays (FPGAs), GPUs, and central processing units (CPUs) are compared across different number of dimensions and precisions. Our evaluation shows that the 100 MHz Virtex-4 xc4vlx160 FPGA design is 4.6 times faster and 25.9 times more energy efficient than a multi-threaded optimized software implementation running on a Xeon W3504 dual-core CPU. It is also 2.6 times faster and 25.4 times more energy efficient than a GPU with comparable silicon process technology.