中文摘要：

The Gaussian Copula Probability Density Function(PDF) plays an important role in the fields of finance,hydrological modeling,biomedical study,and texture retrieval. However,the existing schemes for evaluating the Gaussian Copula PDF are all computationally-demanding and generally the most time-consuming part in the corresponding applications. In this paper,we propose an FPGA-based design to accelerate the computation of the Gaussian Copula PDF. Specifically,the evaluation of the Gaussian Copula PDF is mapped into a fully-pipelined FPGA dataflow engine by using three optimization steps: transforming the calculation pattern,eliminating constant computations from hardware logic,and extending calculations to multiple pipelines. In the experiments on 10 typical large-scale data sets,our FPGA-based solution shows a maximum of 1870 times speedup over a well-tuned singlecore CPU-based solution,and 610 times speedup over a well-optimized parallel quad-core CPU-based solution when processing two-dimensional data.

英文摘要：

The Gaussian Copula Probability Density Function （PDF） plays an important role in the fields of finance, hydrological modeling, biomedical study, and texture retrieval. However, the existing schemes for evaluating the Gaussian Copula PDF are all computationally-demanding and generally the most time-consuming part in the corresponding applications. In this paper, we propose an FPGA-based design to accelerate the computation of the Gaussian Copula PDF. Specifically, the evaluation of the Gaussian Copula PDF is mapped into a fully-pipelined FPGA dataflow engine by using three optimization steps： transforming the calculation pattern, eliminating constant computations from hardware logic, and extending calculations to multiple pipelines. In the experiments on 10 typical large-scale data sets, our FPGA-based solution shows a maximum of 1870 times speedup over a well-tuned single- core CPU-based solution, and 610 times speedup over a well-optimized parallel quad-core CPU-based solution when processing two-dimensional data.