Algorithmic complexity in the heston model: an implementation view

H. Marxen, A. Kostiuk, R. Korn, C. D. Schryver, S. Wurm, I. Shcherbakov, N. Wehn
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引用次数: 3

Abstract

In this paper, we present an in-depth investigation of the algorithmic parameter influence for barrier option pricing with the Heston model. For that purpose we focus on single- and multi-level Monte Carlo simulation methods. We investigate the impact of algorithmic variations on simulation time and energy consumption, giving detailed measurement results for a state-of-the-art 8-core CPU server and a Nvidia Tesla C2050 GPU. We particularly show that a naive algorithm on a powerful GPU can even increase the energy consumption and computation time, compared to a better algorithm running on a standard CPU. Furthermore we give preliminary results of a dedicated FPGA implementation and comment on the speedup and energy saving potential of this architecture.
赫斯顿模型中的算法复杂性:一个实现视图
本文利用Heston模型深入研究了障碍期权定价的算法参数影响。为此,我们着重于单级和多级蒙特卡罗模拟方法。我们研究了算法变化对仿真时间和能耗的影响,给出了最先进的8核CPU服务器和Nvidia Tesla C2050 GPU的详细测量结果。我们特别指出,与在标准CPU上运行更好的算法相比,在强大的GPU上运行朴素算法甚至可以增加能耗和计算时间。此外,我们给出了一个专用FPGA实现的初步结果,并对该架构的加速和节能潜力进行了评论。
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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