An FPGA-Based Data Flow Engine for Gaussian Copula Model

Huabin Ruan, Xiaomeng Huang, H. Fu, Guangwen Yang, W. Luk, S. Racanière, O. Pell, Wenji Han
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引用次数: 2

Abstract

The Gaussian Copula Model (GCM) plays an important role in the state-of-the-art financial analysis field for modeling the dependence of financial assets. However, the existing implementations of GCM are all computationallydemanding and time-consuming. In this paper, we propose a Dataflow Engine (DFE) design to accelerate the GCM computation. Specifically, a commonly used CPU-friendly GCM algorithm is converted into a fully-pipelined dataflow graph through four steps of optimization: recomposing the algorithm to be pipeline-friendly, removing unnecessary computation, sharing common computing results, and reducing the computing precision while maintaining the same level of accuracy for the computation results. The performance of the proposed DFE design is compared with three CPU-based implementations that are well-optimized. Experimental results show that our DFE solution not only generates fairly accurate result, but also achieves a maximum of 467x speedup over a single-thread CPU-based solution, 120x speedup over a multi-thread CPUbased solution, and 47x speedup over an MPI-based solution.
基于fpga的高斯Copula模型数据流引擎
高斯Copula模型(Gaussian Copula Model, GCM)用于对金融资产的依赖性进行建模,在当前金融分析领域中发挥着重要作用。然而,现有的GCM实现都是计算要求高且耗时的。在本文中,我们提出了一个数据流引擎(DFE)设计来加速GCM的计算。具体而言,将一种常用的cpu友好型GCM算法通过重组算法使其对管道友好、去除不必要的计算、共享公共计算结果、降低计算精度同时保持计算结果的相同精度四个优化步骤,转化为全流水线的数据流图。将所提出的DFE设计与三种优化良好的基于cpu的实现进行了性能比较。实验结果表明,我们的DFE解决方案不仅产生了相当准确的结果,而且在基于单线程cpu的解决方案上实现了467倍的加速,在基于多线程cpu的解决方案上实现了120倍的加速,在基于mpi的解决方案上实现了47倍的加速。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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