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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.