使用收缩二项树的低延迟期权定价

Aryan Tavakkoli, David B. Thomas
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引用次数: 6

摘要

本文利用二叉树模型提出了一种新的可重构美式期权定价硬件加速器。所提出的架构在高效和可扩展的收缩解决方案中利用管道和粗粒度并行性,旨在利用现代架构中的大量DSP块。该架构可以在编译时进行调整,以满足用户需求,从将整个FPGA专用于单个选项的低延迟计算,到多个选项的高吞吐量并发评估。在Xilinx Virtex-7 xc7vx980t FPGA上,这使得具有768个时间步长的单个选项可以以小于22微秒的延迟和超过100 K选项/秒的定价速率进行定价。与之前最快的并发选项评估的可重构实现相比,我们在Virtex-4 xc4vs555 FPGA上实现了延迟65倍和吞吐量9倍的改进,其值为10.7 G节点/秒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Low-latency option pricing using systolic binomial trees
This paper presents a novel reconfigurable hardware accelerator for the pricing of American options using the binomial-tree model. The proposed architecture exploits both pipeline and coarse-grain parallelism in a highly efficient and scalable systolic solution, designed to exploit the large numbers of DSP blocks in modern architectures. The architecture can be tuned at compile-time to match user requirements, from dedicating the entire FPGA to low latency calculation of a single option, to high throughput concurrent evaluation of multiple options. On a Xilinx Virtex-7 xc7vx980t FPGA this allows a single option with 768 time steps to be priced with a latency of less than 22 micro-seconds and a pricing rate of more than 100 K options/sec. Compared to the fastest previous reconfigurable implementation of concurrent option evaluation, we achieve an improvement of 65 x in latency and 9x in throughput with a value of 10.7 G nodes/sec, on a Virtex-4 xc4vsx55 FPGA.
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